quant phd programs

PhD Programs with Quantitative Concentrations

Public policy.

• Social Sciences

Public Health Sciences

Comparative human development, political science.

The University has PhD degree programs in various departments and schools that provide a concentration on quantitative methods. New doctoral applicants who are interested in developing specialty in quantitative methods, depending on their disciplinary interests, may look into one of these degree programs.

Doctoral Programs in Quantitative Methods

Business administration.

Econometrics and Statistics , one of the eight dissertation areas in the Booth School PhD program, is concerned with the combination of economic, mathematical, and computer techniques in the analysis of economic and business problems such as forecasting, demand and cost analyses, model-building, and testing empirical implications of theories. Study in this area integrates a comprehensive program of course work with extensive research. The program is designed for students who wish to do research in statistical methods that are motivated by business applications. Students are able to design an individual program of study by combining courses in specific areas of business, such as economics, finance, accounting, marketing, or international business with advanced courses in statistical methods.  Empirical work has always been an important part of the research effort at Chicago Booth in all fields of study. Econometrics and statistics courses are thus useful choices in satisfying the basic discipline or coordinated sequence.

Quantitative Methods  are a key component of the Core curriculum. Specialized Fields in Quantitative Methods include:

• Quantitative Study of Inequality
• Applied econometrics

          Tools of Policy Analysis provides in-depth and technical expertise that can be applied to a broad range of subject          areas.The following are included among the five specialties: Program evaluation, statistics, and survey methods. 

Methods in Human Development Research . Research on human development over the life span and across social and cultural contexts thrives on multiple theoretical perspectives. This research requires creation and improvement of a wide range of research methods appropriately selected for and tailored to specific human development problems. Faculty in the department employ research methods that span the full range from primarily qualitative to primarily quantitative and to strategic mix of both. Across all the substantive domains in Comparative Human Development, theoretical understanding is greatly advanced by methodology; therefore the Department pays serious attention to research design, data collection, analytic strategies, and presentation, evaluation, and interpretations of evidence. The Department has contributed some of the most influential work on psychological scaling on the basis of the item response theory (IRT), multivariate statistical methods, analysis of qualitative data, modeling of human growth, and methods for cross-cultural analysis. Current research interests include (a) assessment of individual growth and change in important domains of development that are often intertwined, (b) examination and measurement of the structure, process, and quality of individual and group experiences in institutionalized settings such as families, schools, clinics, and neighborhoods, and (c) evaluation of the impact of societal changes or interventions on human development via changes in individual and group experiences, with particular interest in the heterogeneity of growth, process, and impact across demographic sub-populations and across social cultural contexts.

Concentration in Biostatistics ,The PhD program in the Department of Public Health Sciences is supported by a core methodological curriculum in population-based research on human health. Students completing a concentration in biostatistics will be prepared to develop state-of-the-art quantitative reasoning and techniques of statistical science, mathematics, and computing, and to apply these to current and future research problems in biomedical science and population health. In addition, these students will complete a minor program of study in a substantive area of application. As such they will be particularly well prepared to engage in collaborative population-based health research. 

Methodology  is one of the five fields in the department. Many students choose the department’s introductory sequence in quantitative methods, followed by more advanced seminars in data analysis and model building. Students with more advanced methodological skills can take further coursework in the department or related courses in economics, public policy or statistics.

Special Fields in Methodology . The Department of Sociology at the University of Chicago has a rich group of faculty members who provide graduate training and conduct research in methods and models for sociological research. These methods can be divided roughly into four categories: Field and ethnographic methods; statistical methods; survey and related methods; and mathematical modeling methods. PhD. students are required to demonstrate competence in two special fields. The Special Field Requirement is generally met during the third and fourth years of graduate study. Students must pass the Preliminary Examination at the PhD. level before meeting the Special Field Requirement. This requirement may be met in three ways: by examination, with a review essay, or through a specified sequence of methods courses. Five types of special fields in methodology are recognized: (1) social statistics, (2) survey research methods, (3) qualitative methods (4) methodology for social organization research, and (5) mathematical sociology.

You are using an outdated browser. This website is best viewed in IE 9 and above. You may continue using the site in this browser. However, the site may not display properly and some features may not be supported. For a better experience using this site, we recommend upgrading your version of Internet Explorer or using another browser to view this website.

- Download the latest Internet Explorer - No thanks (close this window)

• Penn GSE Environmental Justice Statement
• Philadelphia Impact
• Global Initiatives
• Diversity & Inclusion
• Catalyst @ Penn GSE
• Penn GSE Leadership
• Program Finder
• Academic Divisions & Programs
• Professional Development & Continuing Education
• Teacher Programs & Certifications
• Undergraduates
• Dual and Joint Degrees
• Faculty Directory
• Research Centers, Projects & Initiatives
• Lectures & Colloquia
• Books & Publications
• Academic Journals
• Application Requirements & Deadlines
• Tuition & Financial Aid
• Campus Visits & Events
• International Students
• Options for Undergraduates
• Non-Degree Studies
• Contact Admissions / Request Information
• Life at Penn GSE
• Penn GSE Career Paths
• Living in Philadelphia
• DE&I Resources for Students
• Student Organizations
• Career & Professional Development
• News Archive
• Events Calendar
• The Educator's Playbook
• Find an Expert
• Race, Equity & Inclusion
• Counseling & Psychology
• Education Innovation & Entrepreneurship
• Education Policy & Analysis
• Higher Education
• Language, Literacy & Culture
• Teaching & Learning
• Support Penn GSE
• Contact Development & Alumni Relations
• Find a Program
• Request Info
• Make a Gift
• Current Students
• Staff & Faculty

Search form

Quantitative methods, doctor of philosophy (ph.d.), you are here, a doctoral program focused on measurement and evaluation that trains students to create new research methodologies and design empirical data analyses. .

The Quantitative Methods Ph.D. program is designed to prepare future professors at research universities and principal investigators at research and assessment organizations in education, psychology, and related human services fields.

What Sets Us Apart

About the program.

Rigorous coursework across the field of education will prepare students with the tools needed to conduct cutting-edge research and assessment.  

Fall: 4 courses; Spring: 4 courses

Research apprenticeship Yes

Culminating experience Dissertation

The Ph.D. program in Quantitative Methods is designed to prepare students for faculty positions at universities as well as important responsibilities at research and assessment organizations. Graduates will be prepared to design first-rate empirical research and data analyses and to contribute to the development of new research methodologies. Students who apply directly to the doctoral-level study program following a baccalaureate degree will enroll in the core courses described for the  M.S.Ed. degree in Statistics, Measurement, Assessment, and Technology (SMART)  and the more advanced courses for the Ph.D. degree. This will include the development of independent empirical research projects.

Doctoral degree studies include advanced graduate coursework, a research apprenticeship, a Ph.D. Candidacy Examination, and the completion of a doctoral dissertation that represents an independent and significant contribution to knowledge. The research apprenticeship provides students with an opportunity to collaborate with a faculty sponsor on an ongoing basis and to participate in field research leading to a dissertation. 

For information about courses and requirements, visit the  Quantitative Methods Ph.D. program in the University Catalog .

Our Faculty

Affiliated Faculty

Eric T. Bradlow K.P. Chao Professor, The Wharton School Ph.D., Harvard University

Timothy Victor   Adjunct Associate Professor, Penn GSE 

"Penn GSE’s Quantitative Methods Ph.D. program equipped me with the methodological skills to do impactful applied education research as soon as I graduated."

Anna Rhoad-Drogalis

Our graduates.

Graduates go on to careers as university professors, researchers and psyshometricians for government agencies, foundations, nonprofits organizations, and corporations. 

Alumni Careers

• Assistant Professor, Texas A&M University-Corpus Christi
• Associate Director, Bristol-Myers Squibb
• Lead Psychometrician, American Institute of Certified Public Accountants
• Research Analyst, Penn Child Research Center, University of Pennsylvania
• Senior Director, Educational Testing Service
• Senior Researcher, Mathematica

Admissions & Financial Aid

Please visit our Admissions and Financial Aid pages for specific information on the application requirements , as well as information on tuition, fees, financial aid, scholarships, and fellowships.

Contact us if you have any questions about the program.

Graduate School of Education University of Pennsylvania 3700 Walnut Street Philadelphia, PA 19104 (215) 898-6415 [email protected] [email protected]

Christine P. Lee Program Manager (215) 898-0505 [email protected]

Please view information from our Admissions and Financial Aid Office for specific information on the cost of this program.

All Ph.D. students are guaranteed a full scholarship for their first four years of study, as well as a stipend and student health insurance. Penn GSE is committed to making your graduate education affordable, and we offer generous scholarships, fellowships, and assistantships.

Related News & Research

What the recent antitrust settlement means for the NCAA

Penn GSE’s Center for Benefit-Cost Studies of Education makes the case for $5.1 billion education funding overhaul in Pennsylvania

Penn gse's center for benefit-cost studies of education releases new policy brief on pennsylvania's basic education funding proposal.

With pandemic stimulus funds sunsetting, Penn GSE expert offers investment ideas

Center on Standards, Alignment, Instruction, and Learning

The Center on Standards, Alignment, Instruction, and Learning (C-SAIL) examines how college- and career-ready standards are implemented, if they improve student learning, and what instructional tools measure and support their implementation.

Penn Early Childhood and Family Research Center

The Penn Early Childhood and Family Research Center aims to advance the use of science in a context of public trust to address problems affecting the well-being of young children and families facing systemic injustice and disadvantage.

You May Be Interested In

Related programs.

• Education Policy M.S.Ed. 
• Education, Culture, and Society Ph.D. 
• Higher Education Ph.D. 
• Quantitative Methods M.Phil.Ed.
• Statistics, Measurement, Assessment, and Research Technology M.S.Ed.

Related Topics

You are using an outdated browser. Please upgrade your browser to improve your experience. Thanks!

• Welcome from the Chair
• Giving Back
• Job Opportunities
• Affiliated Faculty
• Graduate Students
• AMS Alumnus News & Events
• Undergraduate Program
• Course Offerings
• Course Schedules
• Actuarial Training
• Graduate Program
• Computational Applied Mathematics
• Computational Biology
• Operations Research
• Quantitative Finance
• Data Science
• Research Affiliations

Quantitative Finance MS and PhD  

Figure: Implied Volatility Surface

SPECIAL QUALITIES OF STONY BROOK QUANTITATIVE FINANCE PROGRAM

Most of the Applied Mathematics faculty teaching quantitative finance courses have extensive experience building quantitative trading systems on Wall Street. Because of their Wall Street backgrounds, our faculty are able to place many of their QF students in  internships  during the summer and the academic year at hedge funds and major investment companies. Few other QF programs offer internships. There is limited use of adjunct faculty who come to campus one or two evenings a week after work.

Figure: Merger Arbitrage Strategy

In the world of finance, the name 'Stony Brook' is famous for Renaissance Technologies, which is located a mile from the Stony Brook campus and headed by the former chairman of the Stony Brook Mathematics Department. Renaissance's flagship Medallion Fund has been the best performing hedge fund in the world for the past 20 years. One of the key creative minds at Renaissance, Robert Frey, Stony Brook Applied Mathematics PhD 1986, returned to Stony Brook in 2005 after early retirement at Renaissance to develop a Quantitative Finance program in the Stony Brook Applied Mathematics Department. Frey is chairman of the advisory committee to the University of Chicago's mathematical finance program, the country's best-ranked program in this area. 

The Stony Brook Quantitative Finance program is unique among mathematical sciences departments in its very practical focus on 'alpha generation', Wall Street term for trading strategies for making money. Courses are centered on projects where students use real tick data to analyze and predict the performance of individual stocks and commodities, market indices and derivatives. Also, Stony Brook is one of a small number of quantitative finance programs offering PhD as well as MS training. Our PhDs have taken positions both in Wall Street firms and in university quantitative finance programs. For more information about our quantitative finance courses and faculty, see  QF Courses  and  QF People .

Figure: New York Stock Exchange

Course Requirements for the Quantitative Finance Track

The standard program of study for the M.S. degree specializing in quantitative finance consists of : 

AMS 507   Introduction to Probability AMS 510   Analytical Methods for Applied Mathematics and Statistics AMS 511   Foundations of Quantitative Finance AMS 512   Portfolio Theory AMS 513   Financial Derivatives and Stochastic Calculus AMS 514   Computational Finance AMS 516   Statistical Methods in Finance AMS 517   Quantitative Risk Management AMS 518   Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization AMS 572   Data Analysis

Quantitative Finance Track Electives (students must take at least  2 elective courses  to achieve at least  36  graduate credits along with the required courses):  AMS 515 Case Studies in Machine Learning and Finance  AMS 520 Machine Learning in Quantitative Finance AMS 522 Bayesian Methods in Finance AMS 523 Mathematics of High Frequency Finance AMS 526 Numerical Analysis I  AMS 527 Numerical Analysis II  AMS 528 Numerical Analysis III  AMS 530 Principles of Parallel Computing  AMS 540 Linear Programming AMS 542 Analysis of Algorithms AMS 550 Stochastic Models AMS 553 Simulation and Modeling AMS 560 Big Data Systems, Algorithms and Networks AMS 561 Introduction to Computational and Data Science AMS 562 Introduction to Scientific Programming in C++ AMS 569 Probability Theory I AMS 570 Introduction to Mathematical Statistics AMS 578 Regression Theory AMS 580 Statistical Learning AMS 586  Time Series AMS 595   Fundamentals of Computing AMS 603   Risk Measures for Finance and Data Analysis

Elective courses in the QF program are split in five focus areas. Students can follow one of the following course sequences depending upon their interests.

(A) Typical course sequence:   Modelling and risk management in finance

• First Semester - AMS  507 ,  510 ,  511 , 572 ( or Electives: AMS 520 for those who have already taken an equivalent data analysis course before and have experience with Python)
• Second Semester - AMS  512 ,  513 ,  517 (Electives: AMS 515 , 522 , 523 , 603 )
• Third Semester - AMS  514 ,  516 , 518 (Electives: AMS 553 )

 (B) Typical course sequence:   Machine learning and big data

• First Semester - AMS  507 ,  510 ,  511 , 572 (or Elective AMS 520 for those who have already taken an equivalent data analysis course before and have experience with Python)
• Second Semester - AMS  512 ,  513 ,  517 (Electives: AMS 515 , 560 , 580 )
• Third Semester - AMS  514 ,  516 , 518 (Electives: AMS 586 )

 (C) Typical course sequence:   Statistics and data analytics

• First Semester - AMS  507 ,  510 ,  511 , 572 (or Elective AMS 520 for those who have already taken an equivalent data analysis course before and have experience with Python)
• Second Semester - AMS  512 ,  513 ,  517 (Electives: AMS 515 , 570 , 578 (with pre-requisite 572  )
• Third Semester - AMS  514 ,  516 , 518 (Electives: AMS 553 , 586 ) 

  (D) Typical course sequence:   Stochastic calculus, optimization, and   operation research

• Second Semester - AMS  512 ,  513 ,  517 (Electives: AMS 515 , 542 , 550 , 569 )
• Third Semester - AMS  514 ,  516 , 518 (Electives: AMS 540 , 553 )

(E) Typical course sequence: Computational methods and algorithms

• Second Semester - AMS  512 ,  513 ,  517 (Electives: AMS 515 , 527 , 528 , 561 )
• Third Semester - AMS  514 ,  516 , 518 (Electives: AMS 530 , 562 , 526 (co-requisite or pre-requisite 595 or 561 )

Note 1 : If you have poor programming skills take the following electives (instead of electives recommended in sequences) : AMS 595  Fundamentals of computing (Fall semester) or AMS 561 Introduction to computational and data science (Spring semester). Programming skills are critically important for industrial jobs. Note 2: If a 4th semester becomes necessary, a required course will be needed to continue.

For Ph.D. requirements please click here .

Quantitative Finance Opportunities for Applied Mathematics Graduate Students in Other Tracks Any strong student (3.5+ GPA in first-semester core courses) in another track may enroll in AMS 511 , Foundations in Quantitative Finance.  Selected students, with the permission of the Director of the Center for Quantitative Finance, may take additional quantitative finance courses. Students are eligible to earn an  Advanced Certificate in Quantitative Finance . You must formally apply for the secondary certificate program prior to taking the required courses. Only a maximum of six credits taken prior to enrolling in the certificate program may be used towards the requirements.  The 15-credit advanced certificate requires AMS 511 ,  AMS 512 ,  AMS 513 ,  one additional Quantitative Finance Graduate course  elective, and one additional Applied Mathematics course chosen with an advisor’s approval.

To apply, download the registration form, please click here .

For gainful employment disclosure information for our Quantitative Finance Program, please contact the AMS Department.

• Discrimination
• Sexual Misconduct
• Accessibility Barrier

• Skip to Content
• Catalog Home
• Institution Home
• Graduate Catalog /
• Graduate School of Education /

Quantitative Methods, PhD

The Ph.D. program in Quantitative Methods is designed to prepare students for faculty positions at universities and important responsibilities at research and assessment organizations. Graduates will be prepared to design first rate empirical research and data analyses and to contribute to development of new research methodologies.

Doctoral degree studies include advanced graduate coursework, a research apprenticeship, a Ph.D. Candidacy Examination, and the completion of a doctoral dissertation that represents an independent and significant contribution to knowledge. The research apprenticeship provides students with an opportunity to collaborate with a faculty sponsor on an ongoing basis and to participate in field research leading to a dissertation.

Students who apply directly to the doctoral-level study program following a baccalaureate degree will enroll in the core courses described for M.S.Ed. degree in SMART and the more advanced courses for the Ph.D. degree. This will include the development of independent empirical research projects.

For more information: http://www.gse.upenn.edu/qm/phd

View the University’s Academic Rules for PhD Programs .

The Ph.D. degree program in Quantitative Methods requires a minimum of 20 course units or relevant courses and advanced degree accomplishments. A maximum of eight (8) credits from other institutions may be taken into account in reducing this basic requirement where appropriate.

Required Milestones

Qualifications evaluation (also known as program candidacy).

A Qualifications Evaluation of each student is conducted after the completion of 6 but not more than 8 course units. The evaluation is designed by the specialization faculty and may be based on an examination or on a review of a student’s overall academic progress.

Preliminary Examination (Also known as Doctoral Candidacy)

A Candidacy Examination on the major subject area is required.  The candidacy examination is a test of knowledge in the student's area of specialization, requiring students to demonstrate knowledge and reasoning in the key content areas in their specialization as defined by their academic division. This examination is normally held after the candidate has completed all required courses.

Oral Proposal

All doctoral candidates must present their dissertation proposals orally and in person to the dissertation committee.

Final Defense of the Dissertation

The final dissertation defense is approximately two hours in length and is based upon the candidate’s dissertation. 

The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2024 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.

Print Options

Print this page.

The PDF will include all information unique to this page.

Psychological Sciences

  Alert Content Goes Here

• Graduate Program

Quantitative Methods

Program overview.

Faculty in the Quantitative Methods (QM) program train students in state-of-the-art statistical methods and engage in research that develops and applies such methods. Students in the QM doctoral program develop expertise in the principles of research design and in the theoretical foundations and application of advanced statistical models for human behavior. Students work closely on research projects with a faculty mentor throughout their graduate career, and often collaborate with other faculty and students. QM faculty collectively have expertise in factor analysis and structural equation modeling; network analysis; measurement and item response theory; exploratory data analysis; mediation and moderation; longitudinal methods; multilevel modeling; mixture modeling; categorical data analysis; and generalized linear models. Quantitative faculty approach the study of these topics from a variety of angles, such as: developing computational tools to promote the use of new or existing methods; evaluating the performance of such methods under real-world conditions; and applying these methods in novel and sophisticated ways to solve substantive problems. Several QM faculty have substantive specializations in, for example, individual differences, personality psychology, clinical psychology, learning sciences, and developmental psychology, which facilitate intensive investigation of analytic approaches critical to those substantive domains. Students may pursue greater or lesser degrees of substantive psychological training, in addition to quantitative training, depending on their and their advisors' interests.

The QM program is housed within the Department of Psychology and Human Development at Peabody College-- a top-ten ranked school of education for the past ten years. This unique context exposes QM students to a variety of applications, methods, and statistical problems that arise in psychological and educational research, as well as the social sciences more generally.

QM faculty teach courses on a broad variety of fundamental and advanced topics in design and data analysis. These courses are attended by students from a variety of social science disciplines, as well as by QM students. QM students are encouraged to tailor their curriculum to maximize relevancy for their particular research interests, background, and career goals. QM course offerings include correlation and regression; analysis of variance; psychological and educational measurement; data science methods; multivariate analysis; psychological, field, and clinical research methods; item response theory (basic and advanced); exploratory/graphical data analysis; structural equation modeling; factor analysis; latent growth curve modeling; categorical data analysis; multilevel modeling; mixture modeling; nonparametric statistics; individual differences; causal analysis in field experiments and quasi-experiments; network analysis; statistical consulting; and meta-analysis. Additionally, many of our students get an optional Minor in Biostatistics . Students may also take courses in Scientific Computing , and/or other areas of psychology and education. Several research centers on campus also provide QM students with training opportunities. Vanderbilt’s new Data Science Institute (DSI) offers numerous workshops, short courses, colloquia, and collaboration opportunities using data science methods and tools. QM faculty also serve as teaching faculty and/or faculty affiliates of the DSI and are involved with the development, operations, and strategic goals of the DSI. Also, the Vanderbilt Kennedy Center maintains a statistics and methodology core which provides a methodology lecture series as well as statistical consulting training and resources. Additionally, students gain presentation and research skills by participating in the Quantitative Methods Forum (schedule below).

Core faculty

More information about individual faculty's research programs can be obtained from their websites by clicking on their names. Alternately, a list of QM faculty is available here . Prospective students are encouraged to contact core QM faculty with shared interests to ask questions about the program. Core QM faculty recruit and train Ph.D. students through the QM program.

• * Sun-Joo Cho (item response theory; generalized latent variable modeling; test development and validation)
• * Alex Christensen (network analysis; data science; psychometrics; measurement)
• David Cole (structural equation modeling; mediation analysis; longitudinal methods; developmental psychopathology)
• Shane Hutton (survival analysis; dynamical systems modeling)
• David Lubinski (measurement; assessment; individual differences; intellectual talent development)
• Kristopher Preacher (structural equation modeling; multilevel modeling; mediation and moderation)
• Sonya Sterba (mixture models; multilevel and longitudinal methods; latent variable models)
• Chris Strauss (measurement and assessment; multilevel measurement; structural equation modelling)
• Hao Wu (model evaluation; uncertainty quantification; robust and nonparametric methods; structural equation modeling)

         (* = interested in recruiting a QM Ph.D. student to start in the 2025-2026 academic year)

Emeritus faculty

• Joseph Rodgers (general multivariate methods; exploratory/graphical data analysis; multidimensional scaling and measurement; behavior genetics; adolescent development)
• Jim Steiger (structural equation modeling; model evaluation; inferential methods; statistical computing)
• Andrew Tomarken (categorical data analysis; generalized linear models; longitudinal methods; clinical psychology)

Affiliated faculty

• Li Chen (statistical consulting; quantitative pedagogy)
• Scott Crossley (natural language processing)
• Will Doyle (data science; education policy)
• Kelly Goldsmith (business analytics, marketing, consumer psychology)

The program maintains its own quantitative computer lab, and additionally individual faculty have labs and computing resources that support their research programs. There are also computing labs in the department and elsewhere in Peabody College that are supplied with statistical software often used for classroom teaching.  Special funds for research-related software and computing equipment, as well as external workshop and conference travel, are available to QM students.

Information for Prospective QM Applicants

QM doctoral program graduates are prepared for faculty positions in academic settings, methodology positions in basic or applied research centers, or methodology positions in industry. Students work together with their advisor and advisory committee to refine their career goals, and tailor their research, coursework, and teaching experiences accordingly. The American Psychological Association reports that there are far more jobs for doctoral students trained in quantitative methods in psychology than there are applicants. Further information can be found here , here , and here .

The QM program is designed to lead to a Ph.D. degree within 5 years. In the first two years, students take a series of fundamental methods courses and begin working on research with their advisor. To build students' oral presentation skills, students present their research to the program on a yearly basis. Students who did not enter with a full year of calculus also complete such coursework in the Mathematics Department during this time. In their third year, students complete their masters thesis and continue research in collaboration with their advisor and others, while furthering their expertise with an individualized set of advanced coursework. Students take an exam in their third or fourth year that is based on reading lists related to content in courses they have taken up until that point. In their fourth and fifth years students finish their coursework and conduct a dissertation project under the guidance of their advisor and other committee members, while building additional independent research and/or teaching skills relevant to their particular career goals.

Doctoral applicants admitted to the QM program receive a guaranteed 5 years of stipend and tuition support, which usually takes the form of a combination of research assistantships and/or teaching assistantships in quantitative courses (for instance, the introductory graduate statistics sequence). Additionally, QM students have a successful track record of obtaining prestigious NSF fellowships. Senior students routinely also may obtain other kinds of stipends as statistical analysts or consultants for various research projects and grants on campus; these opportunities serve as valuable supplementary training experiences. Some students also serve as teaching instructors for their own section of an undergraduate statistics course or undergraduate measurement course in order to deepen their teaching credentials. Application instructions are available here .

QM Masters Program

In Spring 2014, the QM program launched a terminal M.Ed. in Quantitative Methods. This program is distinct from our longstanding research-focused Ph.D. program. More information about the goals and expectations for applicants to our M.Ed. program can be found here .

Graduate QM Minor

Doctoral students outside the QM program may elect to minor in quantitative methods. This formal minor involves taking four advanced methods courses from the QM program beyond the first year required graduate statistics sequence (6 courses total). The minor requires a 3.5 average GPA (for all 6 minor courses), with no grade below a B. The minor provides students with exceptional training in the application of complex psychometric and statistical procedures and provides students with skills that can enhance the quality of their research program over the course of their career. Many students find that the credential of a graduate minor in quantitative methods is a valuable asset in the pursuit of research-oriented academic positions or quantitatively-oriented industry positions after graduation. Detailed information on minor requirements can be obtained from the Psychological Sciences graduate student handbook. For more information, contact Kris Preacher .

Undergraduate QM Minor

The QM program offers an 18-credit undergraduate minor in quantitative methodology. For information on our new undergraduate QM minor, please click here .

Quantitative Methods Colloquium Series

The QM program offers a weekly Quantitative Methods Colloquium Series which covers novel methodological advances, cutting-edge applications of quantitative methods, inclusivity in QM, teaching pedagogy in QM, QM professional development activities, QM outreach, and QM workshops. The QM colloquium series features a mix of external speakers from different settings (e.g., academia and industry) and different stages of their careers in order to expose our QM students to a variety of career paths and perspectives. Each semester our QM forum also contains internal program speakers, QM students and QM faculty, to allow us to share our research with, and gain feedback from, our colleagues. For more information on the QM Colloquium please visit the Colloquium schedule .

Quantitative Methods Outreach

At least once per year the QM Colloquium Series features an Open House where statistical consulting problems presented by Peabody faculty guest(s) receive a program-level discussion. Additionally, our QM program offers a statistical consulting course on a yearly basis to which Peabody faculty can submit statistical problems to serve as student projects. QM faculty also maintain a listserv ([email protected]) to which Peabody faculty can submit statistical problems that are limited in scope. Submitted questions will first be considered for open house or course project slots and secondarily for a graduate assistant to the QM faculty for further attention.

Fall 2024 QM Course Offerings

• PSY-GS 8861-01: Statistical Inference . TR 1:15p - 2:30p Hutton
• PSY-GS 8870-01 / PSY-PC 3735-01: Correlation and Regression . TR 9:30a - 10:45a Strauss
• PSY-GS 8873-01: Structural Equation Modeling . TR 11:00a - 12:15p Cole
• PSY-GS 8876-01 / PSY-PC 3724-01: Psychological Measurement / Psychometrics . T 4:15p - 7:05p Lubinski
• PSY-GS 8878-01 / PSY-PC 7878-01: Statistical Consulting . T 1:15p - 4:05p Strauss
• PSY-GS 8879-01 / PSY-PC 3743-01: Factor Analysis . F 10:10a - 1:00p Preacher
• PSY-GS 8882-01: Multilevel Modeling . W 10:10a - 1:00p Preacher

Undergraduate

• PSY-PC 2110-01: Introduction to Statistical Analysis . TR 11:00a - 12:15p Hutton
• PSY-PC 2110-05: Introduction to Statistical Analysis . MWF 11:15a - 12:05p Chen
• PSY-PC 2110-06: Introduction to Statistical Analysis . MWF 12:20a - 1:10p Osina
• PSY-PC 2110-07: Introduction to Statistical Analysis . MWF 10:10a - 11:0 0a Chen
• PSY-PC 2110-08: Introduction to Statistical Analysis . TR 9:30a - 10:45a Vinci-Booher
• PSY-PC 2110-09: Introduction to Statistical Analysis . TR 1:15p - 2:30p Wu
• PSY-PC 2110-10: Introduction to Statistical Analysis . TR 2:45p - 4:00p Wu
• PSY-PC 3722-01: Psychometric Methods . TR 8:00a - 9:15a Cho

• Youth Program
• Wharton Online

PhD Program

• Program of Study

Wharton’s PhD program in Finance provides students with a solid foundation in the theoretical and empirical tools of modern finance, drawing heavily on the discipline of economics.

The department prepares students for careers in research and teaching at the world’s leading academic institutions, focusing on Asset Pricing and Portfolio Management, Corporate Finance, International Finance, Financial Institutions and Macroeconomics.

Wharton’s Finance faculty, widely recognized as the finest in the world, has been at the forefront of several areas of research. For example, members of the faculty have led modern innovations in theories of portfolio choice and savings behavior, which have significantly impacted the asset pricing techniques used by researchers, practitioners, and policymakers. Another example is the contribution by faculty members to the analysis of financial institutions and markets, which is fundamental to our understanding of the trade-offs between economic systems and their implications for financial fragility and crises.

Faculty research, both empirical and theoretical, includes such areas as:

• Structure of financial markets
• Formation and behavior of financial asset prices
• Banking and monetary systems
• Corporate control and capital structure
• Saving and capital formation
• International financial markets

Candidates with undergraduate training in economics, mathematics, engineering, statistics, and other quantitative disciplines have an ideal background for doctoral studies in this field.

Effective 2023, The Wharton Finance PhD Program is now STEM certified.

• Course Descriptions
• Course Schedule
• Dissertation Committee and Proposal Defense
• Meet our PhD Students
• Visiting Scholars

More Information

• Apply to Wharton
• Doctoral Inside: Resources for Current PhD Students
• Wharton Doctoral Program Policies
• Transfer of Credit
• Research Fellowship

• Quantitative Finance Specialization
• Academic Programs
• Management Science and Analytics (Ph.D.)

The Quantitative Finance specialization in the Ph.D. in Management Science and Analytics program is excellent preparation for either academic careers or for students who want to apply the theoretical, analytical, and quantitative rigor of management science to careers in finance.

Dissertation research in this area may include a wide range of topics such as risk modeling, financial time series analysis, and investment analysis.

Required courses for the Quantitative Finance specialization (three credits per course):

• MSC 621—Corporate Finance
• MSC 623—Investments
• MSC 631—Theory of Finance I
• MSC 633—Theory of Finance II
• MSF 545/MSC 613—Structured Fixed Income Portfolios
• MSF 546/MSC 614—Quantitative Investment Strategies

View the curriculum for the Ph.D. in Management Science (MSC) program and MSC course descriptions .

Career Opportunities

Industry and Research

The specialization in Quantitative Finance prepares students for a wide range of careers in finance, particularly in areas such as investment and commercial banking, trading, and risk management. This background also opens career opportunities across industries in business functions focused on finance, financial modeling, economics, and risk compliance.

Chicago’s position as a global center for finance and fintech, as well as the home to the world’s largest markets in financial derivatives, make it a prime location for internships, networking, and job opportunities for Stuart students in quantitative finance.

Our graduates are ready to step into roles such as:

• Senior quantitative analyst or quantitative analytics manager-economic modeling
• Quantitative developer, senior quantitative modeler, or quantitative risk modeler
• Research data scientist, senior quantitative researcher, or quantitative researcher-asset management
• Portfolio risk analyst, senior quantitative risk analyst, or exotic rates quantitative analyst
• Equity derivatives quantitative strategist or quantitative portfolio strategist
• Senior quantitative markets analyst or machine learning analyst

Students interested in academic careers are supported by strong mentoring relationships with our faculty, opportunities to co-author papers published in prestigious scholarly journals, and help in securing adjunct positions to develop their teaching skills.

As a result, our graduates have launched teaching and research careers as finance faculty members at colleges and universities in the United States and around the world, such as:

• Carnegie Mellon University
• Beijing Normal University
• Lewis University
• Brooklyn College - City University of New York
• Benedictine University
• Northeastern Illinois University
• East China Normal University
• Saint Michael’s College (Vermont)

Learn more...

Why Study for a Mathematical Finance PhD?

I was emailed by a reader recently asking about mathematical finance PhD programs and the benefits of such a course. If you are considering gaining a PhD in mathematical finance, this article will be of interest to you.

If you are currently near the end of your undergraduate studies or are returning to study after some time in industry, you might consider starting a PhD in mathematical finance. This is an alternative to undertaking a Masters in Financial Engineering (MFE), which is another route into a quantitative role. This article will discuss exactly what you will be studying and what you are likely to get out of a PhD program. Clearly there will be differences between studying in the US, UK or elsewhere. I personally went to grad school in the UK, but I will discuss both UK and US programs.

Mathematical finance PhD programs exist because the techniques within the derivatives pricing industry are becoming more mathematical and rigourous with each passing year. In order to develop new exotic derivatives instruments, as well as price and hedge them, the financial industry has turned to academia. This has lead to the formation of mathematical finance research groups - academics who specialise in derivatives pricing models, risk analysis and quantitative trading.

Graduate school, for those unfamiliar with it, is a very different experience to undergraduate. The idea of grad school is to teach you how to effectively research a concept without any guidance and use that research as a basis for developing your own models. Grad school really consists of a transition from the "spoon fed" undergraduate lecture system to independent study and presentation of material. The taught component of grad school is smaller and the thesis component is far larger. In the US, it is not uncommon to have two years of taught courses before embarking on a thesis (and thus finding a supervisor). In the UK, a PhD program is generally 3-4 years long with either a year of taught courses, or none, and then 3 years of research.

A good mathematical finance PhD program will make extensive use of your undergraduate knowledge and put you through graduate level courses on stochastic analysis, statistical theory and financial engineering. It will also allow you to take courses on general finance, particularly on corporate finance and derivative securities. When you finish the program you will have gained a broad knowledge in most areas of mathematical finance, while specialising in one particular area for your thesis. This "broad and deep" level of knowledge is the hallmark of a good PhD program.

Mathematical Finance research groups study a wide variety of topics. Some of the more common areas include:

• Derivative Securities Pricing/Hedging: The technical term for this is "financial engineering", as "quantitative analysis" now encompasses a wide variety of financial areas. Some of the latest research topics include sophisticated models of options including stochastic volatility models, jump-diffusion models, asymptotic methods as well as investment strategies.
• Stochastic Calculus/Analysis: This is more of a theoretical area, where the basic motivation stems from the need to solve stochastic differential equations. Research groups may look at path-dependent PDEs, functional Ito calculus, measure theory and probability theory.
• Fixed Income Modeling: Research in this area centres on effectively modelling interest rates - such as multi-factor models, multi-curve term structure models as well as interest rate derivatives such as swaptions.
• Numerical Methods: Although not always strictly related to mathematical finance, there is a vast amount of university research carried out to try and develop more effective means of solving equations numerically (i.e. on the computer!). Recent developments include GPU-based Monte Carlo solvers, more efficient matrix solvers as well as Finite Differences on GPUs. These groups will almost certainly possess substantial programming expertise.
• Market Microstructure/High-Frequency Modeling: This type of research is extremely applied and highly valued by funds engaged in this activity. You will find many academics consulting, if not contracting, for specialised hedge funds. Research areas include creating limit order market models, high frequency data statistical modelling, market stability analysis and volatility analysis.
• Credit Risk: Credit risk was a huge concern in the 2007-2008 financial crisis and many research groups are engaged in determining such "counterparty risks". Credit derivatives are still a huge business and so a lot of research goes into collateralisation of securities as well as pricing of exotic credit derivatives.

These are only a fraction of the total areas that are studied within mathematical finance. The best place to find out more about research topics is to visit the websites of all the universities which have a mathematical finance research group, which is typically found within the mathematics, statistics or economics faculty.

The benefits of undertaking a PhD program are numerous:

• Employment Prospects: A PhD program sets you apart from candidates who only possess an undergraduate or Masters level ability. By successfully defending a thesis, you have shown independence in your research ability, a skill highly valued by numerate employers. Many funds (and to a lesser extent, banks) will only hire PhD level candidates for their mathematical finance positions, so in a pragmatic sense it is often a necessary "rubber stamp". In investment banks, this is not the case so much anymore, as programming ability is generally prized more. However, in funds, it is still often a requirement. Upon being hired you will likely be at "associate" level rather than "analyst" level, which is common of undergraduates. Your starting salary will reflect this too.
• Knowledge: You will spend a large amount of time becoming familiar with many aspects of mathematical finance and derivatives theory. This will give you a holistic view into the industry and a more transferable skill set than an undergraduate degree as you progress up the career ladder. In addition, you will have a great deal of time to learn how to program models effectively (without the day-to-day pressure to get something implemented any way possible!), so by the time you're employed, you will be "ahead of the game" and will know best practices. This aspect is down to you, however!
• Intellectual Prospects: You are far more likely to gain a position at a fund after completing a PhD than without one. Funds are often better environments to work in. There is usually less stress and a more relaxed "collegiate" environment. Compare this to working on a noisy trading floor, where research might be harder to carry out and be perceived as less important.

I would highly recommend a mathematical finance PhD, so long as you are extremely sure that a career in quantitative finance is for you. If you are still unsure of your potential career options, then a more general mathematics, physics or engineering PhD might be a better choice.

Join the QSAlpha research platform that helps fill your strategy research pipeline, diversifies your portfolio and improves your risk-adjusted returns for increased profitability.

The Quantcademy

Join the Quantcademy membership portal that caters to the rapidly-growing retail quant trader community and learn how to increase your strategy profitability.

Successful Algorithmic Trading

How to find new trading strategy ideas and objectively assess them for your portfolio using a Python-based backtesting engine.

Advanced Algorithmic Trading

How to implement advanced trading strategies using time series analysis, machine learning and Bayesian statistics with R and Python.

Psychology (Quantitative Research Methods), PHD

On this page:, at a glance: program details.

• Location: Tempe campus
• Second Language Requirement: No

Program Description

Degree Awarded: PHD Psychology (Quantitative Research Methods)

The PhD program in psychology with a concentration in quantitative research methods offers an immersive education in advanced statistical techniques and research methodologies that are employed in the conduct of both basic and applied psychological research.

A collaborative, interdisciplinary approach to research empowers students to deepen their understanding and tackle key issues, such as exploring the limits of existing methods, pushing the methodological frontiers forward, evaluating the effectiveness of established and emerging methodologies, and improving the robustness of psychological research through innovative measurements and analytical methods.

What sets this program apart is its distinguished, award-winning faculty, known for their expertise and dedication to training the next generation of psychological methodologists. Alongside the faculty, students gain practical experience and master techniques in the areas of measurement, study design, data analysis, statistical modeling, and evaluation of the utility of new and existing methods.

Graduates of this program emerge as experts in quantitative research who are prepared to make meaningful contributions to the field by developing and applying sophisticated statistical and methodological solutions to address pressing research issues.

Quantitative Faculty       Research Labs

Degree Requirements

Curriculum plan options.

Required Core (3 or 4 credit hours) PSY 502 Professional Issues in Psychology (3) or PSY 531 Multiple Regression in Psychological Research (4)

Concentration (3 credit hours) PSY 533 Structural Equation Modeling (3)

Other Requirements (31 credit hours) PSY 530 Intermediate Statistics (4) PSY 532 Analysis of Multivariate Data (3) PSY 534 Psychometric Methods (3) PSY 536 Statistical Methods in Prevention Research (3) PSY 537 Longitudinal Growth Modeling (3) PSY 538 Advanced Structural Equation Modeling (3) PSY 539 Multilevel Models for Psychological Research (3) PSY 540 Missing Data Analysis (3) PSY 543 Statistical Mediation Analysis (3) PSY 555 Experimental and Quasi-experimental Designs for Research (3)

Electives (22 or 23 credit hours)

Research (12 credit hours)

Culminating Experience (12 credit hours) PSY 799 Dissertation (12)

Additional Curriculum Information Electives are determined in consultation with the student's supervisory committee.

Other requirements courses may be substituted for other courses based on consultation with the student's supervisory committee.

Admission Requirements

Applicants must fulfill the requirements of both the Graduate College and The College of Liberal Arts and Sciences.

Applicants are eligible to apply to the program if they have earned a bachelor's or master's degree from a regionally accredited institution.

Applicants must have a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in the last 60 hours of their first bachelor's degree program or a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in an applicable master's degree program.

All applicants must submit:

• graduate admission application and application fee
• official transcripts
• SlideRoom application and fee
• statement of purpose form
• curriculum vitae or resume
• three letters of recommendation
• proof of English proficiency

Additional Application Information An applicant whose native language is not English must provide proof of English proficiency regardless of their current residency.

ASU does not accept the GRE® General Test at home edition.

To apply to the doctoral program, applicants must follow the instructions on the doctoral program admissions instructions and checklist. It is strongly recommended that applicants download and print the instructions and checklist to ensure completion of the application process and that all required supplemental forms are included.

The Department of Psychology application process is completed online through ASU's graduate admission services, which includes the application form and official transcripts. Application to the Department of Psychology doctoral programs is also completed via SlideRoom, for processing of supplemental application materials. The SlideRoom account requires an additional fee.

Applicants must submit three academic letters of recommendation from faculty members who know the student well. Three letters are required, but four letters of recommendation may be submitted.

Next Steps to attend ASU

Learn about our programs, apply to a program, visit our campus, application deadlines, career opportunities.

Quantitative psychologists possess advanced statistical and methodological expertise applicable to various research challenges. While rooted in psychology, their skills find broad applications in fields such as education, heath, neuroscience and marketing. Graduates of the doctorate in psychology (quantitative research methods) program excel in interdisciplinary collaboration and effective communication of complex ideas.

Potential careers induce roles as:

• consultants
• data scientists
• policy analysts
• psychology professors
• psychometricians
• research scientists

Program Contact Information

If you have questions related to admission, please click here to request information and an admission specialist will reach out to you directly. For questions regarding faculty or courses, please use the contact information below.

• [email protected]
• 480/727-4561

Boston University Academics

Boston University

• Campus Life
• Schools & Colleges
• Degree Programs
• Search Academics
• PhD in Mathematical Finance

The PhD in Mathematical Finance is for students seeking careers in research and academia. Doctoral candidates will have a strong affinity for quantitative reasoning and the ability to connect advanced mathematical theories with real-world phenomena. They will have an interest in the creation of complex models and financial instruments as well as a passion for in-depth analysis.

Learning Outcomes

The PhD curriculum has the following learning goals. Students will:

• Demonstrate advanced knowledge of literature, theory, and methods in their field.
• Be prepared to teach at the undergraduate, master’s, and/or doctoral level in a business school or mathematics department.
• Produce original research of quality appropriate for publication in scholarly journals.

After matriculation into the PhD program, a candidate for the degree must register for and satisfactorily complete a minimum of 16 graduate-level courses at Boston University. More courses may be needed, depending on departmental requirements.

PhD in Mathematical Finance Curriculum

The curriculum for the PhD in Mathematical Finance is tailored to each incoming student, based on their academic background. Students will begin the program with a full course load to build a solid foundation in not only math and finance but also the interplay between them in the financial world. As technology plays an increasingly larger role in financial models, computer programming is also a part of the core coursework.

Once a foundation has been established, students work toward a dissertation. Working closely with a faculty advisor in a mutual area of interest, students will embark on in-depth research. It is also expected that doctoral students will perform teaching assistant duties, which may include lectures to master’s-level classes.

Course Requirements

The minimum course requirement is 16 courses (between 48 and 64 credits, depending on whether the courses are 3 or 4 credits each). Students’ course choices must be approved by the Mathematical Finance Director prior to registration each semester. The following is a typical program of courses.

• GRS EC 701 Microeconomic Theory
• GRS MA 711 Real Analysis
• GRS MA 779 Probability Theory I
• QST FE 918 Doctoral Seminar in Finance
• GRS EC 703 Advanced Microeconomic Theory
• GRS MA 776 Partial Differential Equations
• GRS MA 781 Probability Theory 2
• QST FE 920 Advanced Capital Market Theory
• GRS EC 702 Macroeconomic Theory
• GRS MA 783 Advanced Stochastic Processes
• QST MF 850 Advanced Computational Methods
• QST MF 922 Advanced Mathematical Finance
• GRS EC 704 Advanced Microeconomic Theory
• GRS MA 751 Statistical Machine Learning
• QST MF 810 FinTech Programming
• QST MF 921 Topics in Dynamic Asset Pricing

Additional Requirements

Qualifying examination.

Students must appear for a qualifying examination after completion of all coursework to demonstrate that they have:

• acquired advanced knowledge of literature and theory in their area of specialization;
• acquired advanced knowledge of research techniques; and
• developed adequate ability to craft a research proposal.

Guidelines for the examination are available from the departments. Students who do not pass either the written and/or oral comprehensive examination upon first try will be given a second opportunity to pass the exam. Should the student fail a second time, the student’s case will be reviewed by the Mathematical Finance Program Development Committee (MF PDC), which will determine if the student will be withdrawn from the PhD program. In addition, the PhD fellowship (if applicable) of any student who does not pass either the written and/or oral comprehensive examination after two attempts will be suspended the semester after the exam was attempted.

Dissertation

Following successful completion of the qualifying examination, the student will develop a research proposal for the dissertation. The final phase of the doctoral program is the completion of an approved dissertation. The dissertation must be based on an original investigation that makes a substantive contribution to knowledge and demonstrates capacity for independent, scholarly research.

Doctoral candidates must register as continuing students for DS 999 Dissertation, a 2-credit course, for each subsequent regular semester until all requirements for the degree have been completed. PhD students graduating in September are required to register for Dissertation in Summer Session II preceding graduation.

Academic Standards

Time limit for degree completion.

After matriculation into the PhD program, a candidate for the degree must meet certain milestones within specified time periods (as noted in the table below) and complete all degree requirements within six years of the date of first registration. Those who fail to meet the milestones within the specified time, or who do not complete all requirements within six years, will be reviewed by the PhD PDC and may be dismissed from the program. A Leave of Absence does not extend the six-year time limit for degree completion.

Performance Review

The Mathematical Finance Program Development Committee will review the progress of each doctoral candidate. Students must maintain a 3.30 cumulative grade point average in all courses to remain in good academic standing. Students who are not in good academic standing will be allowed one semester to correct their status. Prior to the start of the semester, the student must submit a letter to the Faculty Director (who will forward it to the PDC) explaining why the student has fallen short of the CGPA requirement and how the student plans to correct the situation. Failure to increase the CGPA to acceptable levels may result in probation or withdrawal from the program, at the discretion of the PhD Program Development Committee (PDC).

Graduation Application

Students must submit a graduation application at least seven months before the date they expect to complete degree requirements. It is the student’s responsibility to initiate the process for graduation. The application is available online and should be submitted through the Specialty Master’s & PhD Center website for graduation in January, May, or August.

If graduation must be postponed beyond the semester for which the application is submitted, students should contact the Specialty Master’s & PhD Center to defer the date. If students wish to postpone their graduation date past the six-year time limit for completion, they must formally petition the PhD Program Development Committee (PDC) for an extension. The petition, which must include the reason(s) for the extension as well as a detailed timetable for completion, is subject to departmental and PDC approval.

PhD degree requirements are complete only when copies of the dissertation have been certified as meeting the standards of Questrom School of Business and have been accepted by Mugar Memorial Library.

Related Bulletin Pages

• Questrom School of Business Courses
• Abbreviations and Symbols

Beyond the Bulletin

• Questrom PhD Program
• Questrom PhD in Mathematical Finance Course Requirements
• Questrom PhD Program Admissions
• Questrom School of Business Undergraduate Program
• Minor in Business Administration & Management
• Minor in Innovation & Entrepreneurship
• Professional Evening MBA (PEMBA)
• Online MBA (OMBA)
• Dual Degree MBA Programs
• MS in Business Analytics
• MS in Management Studies
• MS in Mathematical Finance & Financial Technology
• PhD in Business Administration & Management
• Graduate Certificate in Business Analytics
• Graduate Certificate in Financial Technology
• Academic and Student Resources
• Honorary, Service, and Professional Organizations

Terms of Use

Note that this information may change at any time. Read the full terms of use .

related websites

• Questrom School of Business

Accreditation

Boston University is accredited by the New England Commission of Higher Education (NECHE).

• © Copyright
• Mobile Version

Graduate School

Quantitative and Computational Biology

General information, program offerings:, department for program:, director of graduate studies:, graduate program administrator:.

The Program in Quantitative and Computational Biology (QCB) is intended to facilitate graduate education at Princeton at the interface of biology and the more quantitative sciences and computation. Administered from The Lewis-Sigler Institute for Integrative Genomics, QCB is a collaboration in multidisciplinary graduate education among faculty in the Institute and the Departments of Chemistry, Computer Science, Ecology and Evolutionary Biology, Molecular Biology, and Physics. The program covers the fields of genomics, computational biology, systems biology, evolutionary and population genomics, statistical genetics, and metabolomics and proteomics.

Program Highlights

An Outstanding Tradition:  Chartered in 1746, Princeton University has long been considered among the world’s most outstanding institutions of higher education, with particular strength in mathematics and the quantitative sciences. Building upon the legacies of greats such as Turing, von Neumann, Tukey, Compton, Feynman, and Einstein, Princeton established the Lewis-Sigler Institute of Integrative Genomics in 1999 to carry this tradition of quantitative science into the realm of biology.

World Class Research:  The Lewis-Sigler Institute and the QCB program focus on attacking problems of great fundamental significance using a mixture of theory, computation, and experimentation.

World Class Faculty:  The research efforts are led by the QCB program’s 50+ faculty, who include a Nobel Laureate, members of the National Academy of Sciences, Howard Hughes Investigators, and numerous faculty who have received major national research awards (e.g., NIH Pioneer, NIH Innovator, Packard, NSF PECASE, NSF CAREER, etc.).

Personalized Education:  A hallmark of any Princeton education is personal attention. The QCB program is no exception. Lab sizes are generally modest, typically 6 – 16 researchers, and all students have extensive direct contact with their faculty mentors. Many students choose to work at the interface of two different labs, enabling them to build close intellectual relationships with multiple principal investigators.

Stimulating Environment:  The physical heart of the QCB program is the Carl Icahn Laboratory, an architectural landmark located adjacent to biology, chemistry, physics, and mathematics on Princeton’s main campus. Students have access to a wealth of resources, both intellectual and tangible, such as world-leading capabilities in DNA sequencing, mass spectrometry, and microscopy. They also benefit from the friendly atmosphere of the program, which includes tea and cookies every afternoon. When not busy doing science, students can partake in an active campus social scene and world class arts and theater events on campus.

Program Offerings

Program offering: ph.d..

Five courses, QCB515, QCB535, QCB537, QCB538, and COS/QCB551, are required for all students, as is a Responsible Conduct in Research (RCR) course. Two elective courses must be taken from the list below, including at least one from the quantitative course list. Courses not on the approved lists may be taken as electives with approval from the DGS.

Note: The full course of study must be reviewed and approved by the Director of Graduate Studies (DGS).

Quantitative Courses (must take at least one)

• APC 524/MAE 506/AST 506 Software Engineering for Scientific Computing
• CBE 517 Soft Matter Mechanics: Fundamentals & Applications
• CHM 503/CBE 524/MSE 514 Introduction to Statistical Mechanics
• CHM 515 Biophysical Chemistry I 
• CHM 516 Biophysical Chemistry II
• CHM 542 Principles of Macromolecular Structure: Protein Folding, Structure, and Design
• COS 511 Theoretical Machine Learning
• COS 524/COS 424 Fundamentals of Machine Learning
• COS 597D Advanced Topics in Computer Science: Advanced Computational Genomics
• COS 597F Advanced Topics in Computer Science: Computational Biology of Single Cells
• COS 597G Advanced Topics in Computer Science: Understanding Large Language Models
• COS 597O Advanced Topics in Computer Science: Deep Generative Models: Methods, Applications & Societal Considerations 
• ELE 535 Machine Learning and Pattern Recognition
• MAE 567/CBE 568 Crowd Control: Understanding and Manipulating Collective Behaviors and Swarm Dynamics
• MAE 550/MSE 560 Lessons from Biology for Engineering Tiny Devices
• MAT 586/APC 511/MOL 511/QCB 513 Computational Methods in Cryo-Electron Microscopy
• MOL 518 Quantitative Methods in Cell and Molecular Biology
• MSE 504/CHM 560/PHY 512/CBE 520 Monte Carlo and Molecular Dynamics Simulation in Statistical Physics & Materials Science
• NEU 437/537 Computational Neuroscience
• NEU 501 Cellular and Circuits Neuroscience
• NEU 560 Statistical Modeling and Analysis of Neural Data
• ORF 524 Statistical Theory and Methods
• PHY 561/2 Biophysics
• QCB 505/PHY555 Topics in Biophysics and Quantitative Biology
• QCB 508 Foundations of Statistical Genomics

Biological Courses 

• CHM 403 Advanced Organic Chemistry
• CHM/QCB 541 Chemical Biology II
• EEB 504 Fundamental Concepts in Ecology, Evolution, and Behavior II
• EEB 507 Recent Research in Population Biology
• MAE 566 Biomechanics and Biomaterials: From Cells to Organisms 
• MOL 504 Cellular Biochemistry
• MOL 506 Cell Biology and Development
• MOL 521 Systems Microbiology and Immunology
• MOL 523 Molecular Basis of Cancer
• MOL 559 Viruses: Strategy & Tactics
• QCB 490 Molecular Mechanisms of Longevity
• QCB 535 Biological Networks Across Scales: Open Problems and Research Methods of Systems Biology

Selected undergraduate courses of interest (Note: these do not count towards course requirements)

• APC 350 Introduction in Differential Equations
• COS 226 Algorithms and Data Structures
• COS 343 Algorithms for Computational Biology
• EEB 324 Theoretical Ecology
• MOL/QCB 485 Mathematical Models in Biology
• ORF/MAT 309/380 Probability and Stochastic Systems
• QCB 302 Research Topics in QCB
• QCB 311 Genomics  

Additional pre-generals requirements

Research Colloquium: QCB Graduate Colloquium QCB Graduate Colloquium is a research colloquium that has been developed for QCB graduate students, held weekly on an afternoon during the fall and spring terms. First, second, and fourth year graduate students have the opportunity to present their research to peers. 

Rotations All students are required to complete a minimum of three research rotations during their first year of graduate study, with a maximum of four, to explore possible research advisers.

General exam

The general examination is usually taken in January of the second year, and consists of a 7 page written thesis proposal and a 2-hour oral session on the student’s thesis proposal.

Qualifying for the M.A.

The Master of Arts (M.A.) degree is normally an incidental degree on the way to a full Ph.D. and is earned after a student successfully passes the general examination. It may also be awarded to students who, for various reasons, leave the Ph.D. program, provided the student has completed all coursework, pre-generals requirements, and the written portion of the generals examination.

A student must teach a minimum of one full-time assignment (6 AI hours) or teach two part-time assignments of 2 or more AI hours each. Students will typically teach in year 4 of the program.

Post-Generals requirements

Committee Meetings Research progress is overseen by a thesis committee selected by the student after passing the general exam. The committee consists of the thesis adviser(s) and two additional faculty members. At least one member must be QCB faculty. The thesis committee must be approved by the DGS. Annual thesis committee meetings are mandatory. 

Dissertation and FPO

The dissertation and final public oral exam (FPO) are required for all Ph.D. students. All students must write and successfully defend their dissertation according to Graduate School rules and requirements. 

• Ned S. Wingreen

Director of Graduate Studies

Executive committee.

• Brittany Adamson, Molecular Biology
• Joshua Akey, Integrative Genomics
• Julien F. Ayroles, Ecology & Evolutionary Biology
• William Bialek, Physics
• Michelle M. Chan, Molecular Biology
• Thomas Gregor, Physics
• Sarah D. Kocher, Ecology & Evolutionary Biology
• Michael S. Levine, Molecular Biology
• Coleen T. Murphy, Molecular Biology
• Yuri Pritykin, Computer Science
• Joshua D. Rabinowitz, Chemistry
• Joshua W. Shaevitz, Physics
• Stanislav Y. Shvartsman, Chemical and Biological Eng
• Mona Singh, Computer Science
• John D. Storey, Integrative Genomics
• Olga G. Troyanskaya, Computer Science
• Eric F. Wieschaus, Molecular Biology
• Ned S. Wingreen, Molecular Biology
• Martin Helmut Wühr, Molecular Biology

Associated Faculty

• Mohamed S. Abou Donia, Molecular Biology
• Robert H. Austin, Physics
• Bonnie L. Bassler, Molecular Biology
• Clifford P. Brangwynne, Chemical and Biological Eng
• Mark P. Brynildsen, Chemical and Biological Eng
• Curtis G. Callan, Physics
• Daniel J. Cohen, Mechanical & Aerospace Eng
• Ileana M. Cristea, Molecular Biology
• Danelle Devenport, Molecular Biology
• Adji Bousso Dieng, Computer Science
• Tatiana Engel, Princeton Neuroscience Inst
• Jianqing Fan, Oper Res and Financial Eng
• Elizabeth R. Gavis, Molecular Biology
• Zemer Gitai, Molecular Biology
• Frederick M. Hughson, Molecular Biology
• Martin C. Jonikas, Molecular Biology
• Yibin Kang, Molecular Biology
• Andrej Kosmrlj, Mechanical & Aerospace Eng
• Andrew M. Leifer, Physics
• Simon A. Levin, Ecology & Evolutionary Biology
• Jonathan M. Levine, Ecology & Evolutionary Biology
• Lindy McBride, Ecology & Evolutionary Biology
• Tom Muir, Chemistry
• Mala Murthy, Princeton Neuroscience Inst
• Cameron A. Myhrvold, Molecular Biology
• Celeste M. Nelson, Chemical and Biological Eng
• Sabine Petry, Molecular Biology
• Catherine Jensen Peña, Princeton Neuroscience Inst
• Eszter Posfai, Molecular Biology
• Ben Raphael, Computer Science
• Mohammad R. Seyedsayamdost, Chemistry
• Corina E. Tarnita, Ecology & Evolutionary Biology
• Jared E. Toettcher, Molecular Biology
• Samuel S. Wang, Princeton Neuroscience Inst
• Haw Yang, Chemistry
• Ellen Zhong, Computer Science

For a full list of faculty members and fellows please visit the department or program website.

Permanent Courses

Courses listed below are graduate-level courses that have been approved by the program’s faculty as well as the Curriculum Subcommittee of the Faculty Committee on the Graduate School as permanent course offerings. Permanent courses may be offered by the department or program on an ongoing basis, depending on curricular needs, scheduling requirements, and student interest. Not listed below are undergraduate courses and one-time-only graduate courses, which may be found for a specific term through the Registrar’s website. Also not listed are graduate-level independent reading and research courses, which may be approved by the Graduate School for individual students.

CHM 541 - Chemical Biology II (also QCB 541)

Cos 551 - introduction to genomics and computational molecular biology (also mol 551/qcb 551), cos 557 - artificial intelligence for precision health (also qcb 557), mat 586 - computational methods in cryo-electron microscopy (also apc 511/mol 511/qcb 513), qcb 501 - topics in ethics in science (half-term), qcb 505 - topics in biophysics and quantitative biology (also phy 555), qcb 508 - foundations of statistical genomics, qcb 515 - method and logic in quantitative biology (also chm 517/eeb 517/mol 515/phy 570), qcb 570 - biochemistry of physiology and disease, qcb 590 - extramural research internship in quantitative and computational biology.

College of Education

Measurement, quantitative methods, & learning sciences doctoral program.

The University of Houston's Measurement, Quantitative Methods, & Learning Sciences (MQM-LS) doctoral program equips students with the skills necessary to design, conduct and interpret quantitative research projects that help solve our society's most difficult problems. Students develop a broad understanding of psychological and learning theories while also receiving strong quantitative methods training. With these skills, our graduates can measure and analyze a wide variety of topics and issues in psychology and education with unique insights. Students received a wide variety of research opportunities within the Department of Psychological, Health, & Learning Sciences; the College of Education and UH. Our mix of quantitative methods training and learning sciences training produces strong candidates ready to compete in a competitive job market.

• PHLS Faculty
• Mission & Values
• Student & Alumni Profiles

About the Program

• 69 hours of minimum required coursework
• 4 years to complete program when enrolled full-time (at least 9 hrs/semester)
• MQM-LS Student Handbook
• MQM-LS Program at a Glance
• Factors Considered in Graduate Admissions and Awarding of Fellowships
• UH Graduate School

What will I learn while attending the MQM-LS program?

MQM-LS students gain knowledge of measurements and quantitative research methods and theoretical foundations in human development and learning theory through:

• Candidacy research project
• Comprehensive Examination Portfolio
• Dissertation

What can I do with my degree?

Upon completion of the program, graduates will be qualified to enter careers in a varity of roles and settings, including:

• University and college professors
• Researchers in Research and Accountability Divisions of public school systems
• Data analysts or research specialists
• Independent consultants

Important MQM-LS Resources

The following is a collection of important program resources:

• American Psychological Association Division 5 (Quantitative and Qualitative Methods)
• American Psychological Association Division 15 (Educational Psychology)
• American Psychological Association Division 45 (The Society for the Psychological Study of Culture, Ethnicity, and Race)
• American Educational Research Association Division C (Learning and Instruction)
• American Educational Research Association Division D (Measurement & Research Methodologies)

MQM-LS Faculty

The following is a list of current mqm-ls faculty:, dr. weihua fan.

Measurement, Quantitative Methods & Learning Sciences

Faculty Profile | Email

Dr. Allison Master

Dr. margit wiesner.

• PHLS Homepage
• Our Programs

The MQM-LS faculty's research seeks to develop and improve research approaches and techniques while applying them to better understanding issues in psychology, education and youth behavior. Visit the PHLS Research Portal to learn more about our diverse interests and discover faculty pursuing answers to the questions that matter to you. 

Feel free to contact faculty directly to learn more about their research. You can find contact information in the Research Portal or by visiting the COE Faculty Directory .

• PHLS Research Portal

Financial Aid

All MQM-LS doctoral students are encouraged to apply for scholarships through the UH and the College of Education. To learn more about how to fund your graduate studies, visit the Graduate Funding page .

Graduate Tuition Fellowship

Graduate Tuition Fellowship (GTF) provides tuition remission for 9 credit hours, during the academic year, to students who enroll in at least 9 credit hours. During the summer term, GTFs are contingent upon available budget. Not all years in the graduate program may be covered by this program.

Assistantships

Graduate appointments are usually available to students during the first two years of graduate studies. The program doesn't cover mandatory fees or course fees. Not all years in the graduate program are covered by this program. 

To learn more about funding your education, contact the COE's College of Graduate Studies at  [email protected]  or call 713-743-7676.

• COE Financial Aid and Scholarships
• UH Graduate Funding
• UH Graduate Financial Information

Houston, Texas

Houston is the fourth largest city in the United States and one of the nation's most diverse cities. This fact benefits our students and faculty both personally and professionally. Home to more than 100 different nationalities and where more than 60 different languages are spoken, Houston is the perfect environment to practice what you're learning in the classroom. The city also boasts more than 12,000 theater seats and 11,000 diverse restaurants featuring cuisines from around the globe (Don't know where to start? Just ask a Houstonian, and they're sure to bombard you with at least a dozen places to eat.) 

Houston is bustling with culture, energy and offers something for everyone inside and outside the classroom.

(Background photo: “ Metropolis ” by eflon is licensed under CC BY 2.0 .)

• Student Housing & Residential Life
• Greater Houston Partnership - Welcome to Houston

Ready to Apply?

Mqm-ls program application deadline: feb. 1 (domestic students), mqm-ls program application deadline: feb. 1 (international students).

Are you ready to apply to the University of Houston MQM-LS doctoral program ? Yes? You can learn more about the application process by visiting the College of Education's Graduate Admissions page  or jump right into the application process by visiting the UH's How to Apply to Graduate School page .

If you need more information about the MQM-LS program, we are here to help. You can always contact the COE Office of Graduate Studies by phone at 713-743-7676  or by email .

The Measurement, Quantitative and Learning Sciences doctoral program is a member of UH's Psychological, Health, & Learning Sciences department .

Program Director:  Dr. Weihua Fan

UH College of Education Stephen Power Farish Hall 3657 Cullen Blvd., Room 491 Houston, TX 77204-5023

Undergraduate: [email protected] or 713-743-5000 Graduate: [email protected] or 713-743-7676 General: [email protected] or 713-743-5010

• Service to the State

College of Education - UT Austin

• Academics Overview
• Bachelor’s Programs
• Master’s Programs
• Doctoral Programs
• Post-baccalaureate
• Educator Preparation Programs
• Student Life Overview
• Career Engagement
• For Families
• Prospective Students
• Current Students
• Tuition, Financial Aid and Scholarships
• Commencement
• Office of Student Affairs
• Departments Overview
• Curriculum and Instruction
• Educational Leadership and Policy
• Kinesiology and Health Education
• Our Programs
• Educational Psychology
• Special Education
• Centers and Institutes
• Find Faculty
• Office of Educational Research
• Alumni and Friends Overview
• Advisory Council
• Meet Our Alumni
• Update Your Information
• About Overview
• College Leadership
• Facts and Rankings
• Reimagine Education
• Visit the college
• COE Campus Buildings

How to Apply

• How To Apply
• Newly Admitted Students
• Academic Advising
• Student Services
• Office of Educational Research Support
• Administrative Units
• Campus Buildings
• Communications, Marketing and Media
• Visit the College

Quantitative Methods Doctoral Program

Quantitative methods.

Doctoral Program

Department of Educational Psychology

Please note required coursework may vary from year to year. Current students should always defer to their Program of Work for course requirements and consult with their faculty advisor / Graduate Advisor for any needed clarifications.

Quantitative Methods doctoral students are required to complete: 

• EDP Foundation courses,
• QM Program courses, 
• Out-of-Specialization courses, and
• Qualifying Process and Dissertation coursework.

Student coursework may vary depending on prior graduate coursework and waivers. All required courses must be completed with a grade of at least B-. 

Program Details

Goal 1:  Learn to plan and execute sophisticated quantitative research studies, as well as to analyze and evaluate the research carried out by others.

Goal 2:  Acquire expertise in a variety of advanced statistical and psychometric modeling techniques including innovative techniques that are on the cutting edge of the field.

Goal 3:  Learn to develop research designs and analysis strategies that are tailored to and appropriate for specific quantitative research questions, based on an understanding of the relationship between the design, the measures used and the relevant data analysis techniques.

Goal 4:  Develop the problem solving skills needed to serve as a quantitative research consultant.

Goal 5:  Develop the statistical, mathematical, and computing skills needed to conduct methodological research and contribute new methodological knowledge to the field.

Goal 6:  Acquire the deep, conceptual understanding of measurement principles and procedures necessary to develop and understand the proper use and assessment of use of measurement instruments (surveys, questionnaires, etc.) for specific educational, psychological and social science research and evaluation purposes.

Goal 7:  Learn to conduct applied psychometric research and to innovate psychometric techniques.

Goal 8 : Advance the field of quantitative research methodology through exemplary teaching and research, and acquire the professional skills that will support participation and leadership in national research organizations.

Application Requirements

A master’s degree in Quantitative Methods or a related field such as Statistics or Quantitative Psychology is required.

EDP Foundation Courses  (23 credit hours)

The Educational Psychology Foundation courses represent foundational knowledge in educational psychology, and reflect basic knowledge in the breadth of scientific psychology, its history of thought and development, research methods, and applications. Foundation courses must be completed prior to the Qualifying Process.

Methods Foundation  (17 hours)

• Prerequisite Course:  EDP 380C.2 Fundamental Statistics: prerequisite for all Methods courses.
• EDP 480C.6 Statistical Analysis for Experimental Data
• EDP 380D.4 Psychometric Theory and Methods
• EDP 480C.4 Correlation & Regression Methods
• EDP 381C.2 Research Design & Methods for Psychology and Education

Development & Learning Foundation  (6 hours)

Human Development & Social Foundation Courses  (Choose 1) :

• EDP 382C.2 Social Psychology
• EDP 382F.3 Life Span Development

Learning Foundation Courses  (Choose 1) :

• EDP 382D.4 Psychology of Learning
• EDP 382D.6 Motivation and Emotion
• EDP 382D Instructional Psychology

Quantitative Methods Program Courses (30 hours)

• EDP 380D.6 Program Evaluation Models & Techniques
• EDP 380C.8 Data Analysis Using SAS
• EDP 380C Data Exploration and Visualization in R
• EDP 380C Statistical Modeling and Simulation in R
• EDP 380C.14 Structural Equation Modeling
• EDP 380C.16 Hierarchical Linear Modeling
• EDP 380C.12 Survey of Multivariate Methods
• EDP 380C.23 Missing Data Analysis
• EDP 380D.8 Item Response Theory
• EDP 380D.14 Applied Psychometrics
• EDP 381C.14 Causal Inference

Program Electives (12 hours)

An additional 4 QM program electives must also be chosen from the following (or alternative QM program elective approved by Area Chair):

• EDP 380C.18 Applied Bayesian Analysis
• EDP 380D.18 Advanced Psychometrics Research
• EDP 380D.11 Computer Adaptive Testing
• EDP 381E Advanced Item Response Theory
• EDP 381C.12 Meta-Analysis
• EDP 381D Advanced Statistical Modeling
• EDP 380C.22 Analysis of Categorical Data
• EDP 380D.10 Test and Scale Construction

Out-of-Specialization Courses (9 hours)

The Graduate School requires doctoral students to complete 9 hours of coursework outside of their area of specialization. These courses are an opportunity to enhance research/clinical interests and form relationships with out-of-area faculty; course choice must be approved by faculty adviser.

• 1 course (minimum 3 hours) taken outside of the EDP department
• 2 courses (minimum 6 hours) taken either outside of the EDP department, or an EDP program area outside QM.
• At least 1 must be taken for a letter grade

Qualifying Process & Dissertation  (12+ hours)

• Qualifying Process : EDP 395R Qualifying Process Research (2 semesters, no later than the semester in which turn in the Qualifying Document)
• Dissertation : EDP 3/6/999W Dissertation, beginning the semester following advancement to candidacy.

En-Route Masters

EDP doctoral students admitted without a master’s in the field must complete an en-route master’s degree before receiving the doctoral degree.  See the En-Route Master’s page for requirements.

Doctoral Portfolio Programs  (Optional)

Portfolio programs  are optional opportunities for doctoral graduate students to obtain credentials in a cross-disciplinary academic area of inquiry while they are completing the requirements for a degree in a particular discipline. A portfolio program usually consists of four thematically related graduate courses and a research presentation.

Students are admitted to the program area, and while they are welcome to select individual faculty members for their application, they are not required to do so.

Interested in statistical models with a focus on deriving and evaluating multilevel model extensions and meta-analysis models for educational, behavioral, social and medical science data.

Interests include the development and dissemination of computerized adaptive testing applications in educational and psychological testing and patient-reported outcome measurements.

Research interests focus on using Bayesian statistical methods to employ hierarchical linear modeling, specifically working with longitudinal and mediation data.

Statistical methods related to psychometrics, such as uni- and multi-dimensional item response theory, response time modeling, cognitively diagnostic assessment, and stochastic test design.

Quantitative methods for causal inference, experimental/quasi-experimental design and analysis, causal mediation analysis, clustered and/or longitudinal data analysis.

My principal methodological research interest deals with the various facets of model specification, including, but not limited to, model comparison/selection and model modification methods. With the use of simulation techniques, I examine the perform...

Additional Resources

• Frequently Asked Questions
• Current Student Resources

At a Glance

Program Starts : Fall, Summer

Deadline to Apply : December 1

Credit Hours Required : 86

Schedule : Full-time enrollment required until admitted to candidacy

Program Location : On Campus

GRE Required? Yes

Area Chair Hyeon-Ah Kang

Find out information about the admission process and application requirements.

Become a Longhorn

Start your ApplyTexas application today to take the next steps towards your future as a Longhorn.

Request Information

Let us know what your academic interests are within the College of Education and we’ll be in touch.

Educational Psychology News

Discover the exciting research and work being done by faculty and students in the Department of Educational Psychology. 

Graduate Students

• All Graduate Programs
• Graduate Student FAQs
• Master’s Programs

Smart. Open. Grounded. Inventive. Read our Ideas Made to Matter.

Which program is right for you?

Through intellectual rigor and experiential learning, this full-time, two-year MBA program develops leaders who make a difference in the world.

A rigorous, hands-on program that prepares adaptive problem solvers for premier finance careers.

A 12-month program focused on applying the tools of modern data science, optimization and machine learning to solve real-world business problems.

Earn your MBA and SM in engineering with this transformative two-year program.

Combine an international MBA with a deep dive into management science. A special opportunity for partner and affiliate schools only.

A doctoral program that produces outstanding scholars who are leading in their fields of research.

Bring a business perspective to your technical and quantitative expertise with a bachelor’s degree in management, business analytics, or finance.

A joint program for mid-career professionals that integrates engineering and systems thinking. Earn your master’s degree in engineering and management.

An interdisciplinary program that combines engineering, management, and design, leading to a master’s degree in engineering and management.

Executive Programs

A full-time MBA program for mid-career leaders eager to dedicate one year of discovery for a lifetime of impact.

This 20-month MBA program equips experienced executives to enhance their impact on their organizations and the world.

Non-degree programs for senior executives and high-potential managers.

A non-degree, customizable program for mid-career professionals.

PhD Program in Finance

2023-24 curriculum outline.

The MIT Sloan Finance Group offers a doctoral program specialization in Finance for students interested in research careers in academic finance. The requirements of the program may be loosely divided into five categories: coursework, the Finance Seminar, the general examination, the research paper, and the dissertation. Attendance at the weekly Finance Seminar is mandatory in the second year and beyond and is encouraged in the first year.  During the first two years, students are engaged primarily in coursework, taking both required and elective courses in preparation for their general examination at the end of the second year.  Students are required to complete a research paper by the end of their fifth semester, present it in front of the faculty committee and receive a passing grade.  After that, students are required to find a formal thesis advisor and form a thesis committee by the end of their eighth semester. The Thesis Committee should consist of at least one tenured faculty from the MIT Sloan Finance Group.

Required Courses

The following set of required courses is designed to furnish each student with a sound and well-rounded understanding of the theoretical and empirical foundations of finance, as well as the tools necessary to make original contributions in each of these areas. Finance PhD courses (15.470, 15.471, 15.472, 15.473, 15.474) in which the student does not receive a grade of B or higher must be retaken.

First Year - Summer

Math Camp begins on the second Monday in August. 

First Year - Fall Semester

14.121/14.122 Micro Theory I/II

14.451/14.452 Macro Theory I/II ( strongly recommended)

14.380/14.381 — Statistics/Applied Econometrics

15.470 — Asset Pricing

First Year - Spring Semester

14.123/14.124 Micro Theory III/IV

14.453/14.454 Macro Theory III/IV (strongly recommended)

14.382 – Econometrics

15.471 – Corporate Finance

Second Year - Fall Semester

15.472 — Advanced Asset Pricing

  14.384 — Time-Series Analysis or  14.385 — Nonlinear Econometric Analysis  (Enrolled students receive a one-semester waiver from attending the Finance Seminar due to a scheduling conflict)

15.475 — Current Research in Financial Economics

Second Year - Spring Semester

15.473 — Advanced Corporate Finance

 15.474 — Current Topics in Finance (strongly encouraged to take multiple times)

15.475 — Current Research in Financial Economics

Recommended Elective Courses

Beyond these required courses, students are expected to enroll in elective courses determined by their primary area of interest. There are two informal “tracks” in Financial Economics: Corporate Finance and Asset Pricing. Recommended electives are designed to deepen the student's grasp of material that will be central to the writing of his/her dissertation. Students also have the opportunity to take courses at Harvard University. There is no formal requirement to select one track or another, and students are free to take any of the electives.

Quantitative Methods

In an era of dwindling resources and increasing competition, optimization questions have assumed a new and urgent importance . To that end, doctoral seminars in Quantitative Methods focus on advanced optimization applications and methodologies. Related courses are available from areas such as industrial and electrical engineering and computer sciences.

Faculty collaboration with other areas of management and related engineering programs enables students to participate in research on a stimulating range of optimization applications . Current areas of faculty interest in applied optimization include transportation, communication, distribution, and manufacturing systems. Other application domains include auditing, scheduling, and quality control.

A specialization in statistics and its applications address managerial problems in which randomness or uncertainty complicates the decision environment, offering students a rich variety of topics for research. Current faculty research interests in applied statistics include data mining, reliability theory, stochastic marketing models, auditing and acceptance sampling, statistical decision theory, and statistical quality and process control.

Download the Fact Sheet

Request More Information

Most Trusted #4

Morning Consult, 2022

Best Value School #7

The Wall Street Journal / Times Higher Education, 2022

Most Innovative School in the U.S. Top 10

US News and World Report, 2023

Want to Learn More? 

If you would like to receive more information about doctoral study in Quantitative Methods, please fill out the form and an Admissions Specialist will be in touch to connect you with a department representative!

Contact us for more information. [email protected]

Program Details

Faculty and Students

• US Rankings
• UK rankings

Follow along with the video below to see how to install our site as a web app on your home screen.

Note: This feature may not be available in some browsers.

• 2024 Rankings

2024 QuantNet Ranking of Best Financial Engineering Programs

The QuantNet ranking of Financial Engineering, Mathematical, and Quantitative Finance master's programs in the US offers detailed insights into placement and admission statistics from the nation's top programs. It serves as the ultimate guide for prospective applicants, helping them choose and enroll in the best master’s programs in quantitative finance.

Baruch College

Princeton university, carnegie mellon university, university of california, berkeley, columbia university, university of chicago, cornell university, new york university, massachusetts institute of technology.

NYU Tandon School of Engineering

Georgia institute of technology, north carolina state university, university of california, los angeles, johns hopkins university, university of washington, rutgers university, university of illinois urbana-champaign, stevens institute of technology, university of minnesota, boston university, fordham university, university of california, san diego.

*Base + sign on bonus (US only) Eligible STEM degree as designated by DHS for the 24 months OPT extension purpose.

• Published 12/12/23
• Page views 4,070,706

Latest reviews

• Xiangchen Liu
• Thursday at 6:37 PM

Latest tracker

Columbia mfe, princeton mfin enrolled, yale asset management enrolled, boston msmft enrolled, ucl computational finance, georgia tech qcf enrolled, uw seattle compfin, latest posts.

• Latest: quantnabe
• 31 minutes ago
• Yesterday at 11:47 PM
• Latest: jarryds
• Yesterday at 7:11 PM

• Latest: latus_rectum
• Yesterday at 2:55 PM

• Latest: bootstrap
• Yesterday at 2:00 PM

• Latest: savlaaman
• Yesterday at 1:55 PM

• This site uses cookies to help personalise content, tailor your experience and to keep you logged in if you register. By continuing to use this site, you are consenting to our use of cookies. Accept Learn more…

We care about your privacy

Thank you, your preferences have been updated.

By clicking "Accept All", you agree to the storing of cookies on your device to give you the most optimal experience using our website. We may also use cookies to enhance performance, analyse site usage and to personalise your experience.

Quantitative Analytics Program

Explore our quantitative analytics program.

The rotational Quantitative Analytics program is designed to provide you with the opportunity to gain comprehensive professional and industry experience that prepares you to develop, implement, calibrate, and validate various analytical models. Wells Fargo hires a number of PhDs and Master’s Candidates within the Capital Markets, and Risk Analytics and Decision Science teams.

Join our   Talent Community  to receive updates and job alerts associated with your profile. Select "Early Career/College" as your Career Status.

What will you do?

Your responsibilities include, but are not limited to:

Developing and validating models for different uses under the direction of experienced team members according to the track of your choice: 

      The Capital Markets Track  deals with the mathematical models for pricing, hedging and risking complex financial instruments. Wells Fargo trading portfolios include products in all traded asset classes such as credit, commodity, Equity, FX Rate, Mortgages, and Asset-Backed Finance.

      The Risk Analytics & Decision Science Track  deals with the statistical, econometric, and machine-learning/AI models for a variety of applications, including loss and revenue forecasting, credit decisions, financial crimes, fair lending, operational risks, and analysis of unstructured data such as text and audio.

• Use Python, R, C++, SAS, SQL or other programming languages as well as mathematical/statistical packages for model development and validation
• Perform mathematical model development and validation (risk assessment) under the direction of experienced team members
• Produce required documentation to evidence model development or validation
• Understand business needs and providing possible solutions through clear verbal and written communications to management and fellow team members
• Stay up to speed on industry challenges and new and innovative modeling techniques used across Wells Fargo to solve business problems or enhance business capabilities.
• Participate in model related projects for varying purposes, methodologies and relevant lines of business

Is this opportunity right for you?

Program structure and desired qualifications:

• Full-time program for Master's and PhD candidates. This is a 12-month rotational program that starts in July (1-month classroom training followed by two rotations).
• Summer internship for Master's and PhD candidates. Program length is 10 weeks.
• Enrolled in a Master’s or PhD program in: Statistics, Applied or Computational Mathematics, Computer Science, Economics, Physics, Quantitative Finance, Operations Research, Data Science, Engineering or related quantitative field or a related quantitative field
• Excellent computer programing skills and use of statistical software packages such as Python, R, SAS, SQL, Spark, Java, and C++
• Strong verbal, written communication and interpersonal skills
• For the Capital Markets Track : Experience and demonstrated knowledge in mathematical and numerical methods including Monte Carlo methods, differential equations, linear algebra, applied probability, and statistics
• For the Risk Analytics & Decision Science Track : Experience and demonstrated first-hand knowledge in a number of these areas: data analysis, statistical modeling, machine learning/AI models, data management, and computing

What does my future look like?

Upon successful completion of the program, participants will be permanently placed in one of Wells Fargo's model development or model validation groups:

• Artificial Intelligence Machine Learning Model Development
• Traded Products Model Development
• Risk Modeling Group
• Market and Counterparty Risk Analytics
• Mortgage Model Development
• Corporate Model Risk
• Commercial Banking Model Development
• Consumer Modeling

Where are the opportunities?

Summer internship and full-time opportunities are located in Charlotte, NC. Additional locations may be added based on business needs.

Helpful resources

Learn about the Centers of Excellence of the Quantitative Analytics Program.

Learn more about the   application process

Back to   Early Careers page

You are leaving the wellsfargojobs.com website.

You are leaving wellsfargojobs.com and entering a third-party domain. Although Wells Fargo has a relationship with this website, Wells Fargo does not provide the products and services on the website. Please review the applicable privacy and security policies and terms and conditions for the website you are visiting.

Any tools at the following website are provided for educational and illustrative purposes only. Wells Fargo does not guarantee the accuracy of the calculations and their applicability to your circumstances.

Please update your browser.

• Careers Brand
• JPMC Careers
• Careers in United States
• English Master
• Careers Home
• Student & Graduate Careers
• Jobs, Student Programs & Internships

Quantitative Finance Programs

Develop solutions that drive industry innovation.

Our mission is to develop and maintain sophisticated mathematical models, cutting-edge methodologies, and infrastructure to value and hedge financial transactions ranging from vanilla flow products to high and low-frequency trading algorithms.

Program information

Learn more about our Quantitative Finance Programs

What you'll do

Who we're looking for

What we offer

Where To Learn More

Spend your internship working alongside our top tier professionals, driving innovation through financial engineering, derivatives modeling, asset and liability management and risk management. You'll help develop or validate mathematical models, methodologies and tools used throughout the firm while gaining in-depth insight into the world of risk modeling, investment banking and the financial services industry.

Throughout the application process, you will have an opportunity to learn about the diverse group of teams across the firm hiring through the Quantitative Finance Programs. Interns will be placed in a role based on their background and professional interests. Some of these opportunities include:

Markets – Quantitative Research :  Develop and maintain sophisticated mathematical models, cutting-edge methodologies, and infrastructure to value and hedge financial transactions ranging from vanilla flow products to high and low-frequency trading algorithms.

Model Risk Governance & Review:  Work with model developers and the business to review and approve models for actual use and monitor performance for risk measurement.

Treasury and Chief Investment Office:  Best-in-class strategy and quantitative models for asset-liability management (ALM).

Valued qualities

We are seeking colleagues with excellent analytical, quantitative and problem solving skills, as well as demonstrated research skills.

Beyond that, we are most interested in are the things that make you unique: the personal qualities and outside interests and achievements beyond academia that demonstrate the kind of person you are and the difference you could bring to the team.

Mastery of advanced mathematics (probability theory, stochastic calculus, partial differential equations, numerical analysis, statistics, econometrics) and/or the ability to programme using C++ or Python.

Knowledge of options pricing theory, trading algorithms or financial regulations. 

Strong verbal and written communication skills and the ability to present findings to a non-technical audience.

On-the-job experience

You'll be assigned a project that will help develop your analytics and coding skills, while learning the core fundamentals of financial engineering, derivatives modeling, risk management and in particular, machine learning.

Through hands-on work experience and training courses, you'll learn first-hand about market-sector specifics, building your technical skills and industry knowledge. You'll be supported by your teammates, tutors and mentors throughout the internship experience.

Career Progression

The specialized knowledge and skills gained through the program will prepare you for a successful career at the firm. Top performing candidates may receive a full-time offer.

Explore life at JPMorgan Chase with this free & self-paced virtual experience. To learn more and register, visit the Quantitative Research page on Forage.

*Registration or completion of Forage virtual experience programs is optional and will not impact consideration or hiring decisions.

What we do 

How we hire

You're now leaving J.P. Morgan

J.P. Morgan’s website and/or mobile terms, privacy and security policies don’t apply to the site or app you're about to visit. Please review its terms, privacy and security policies to see how they apply to you. J.P. Morgan isn’t responsible for (and doesn’t provide) any products, services or content at this third-party site or app, except for products and services that explicitly carry the J.P. Morgan name.

APPLY for QBS PhD

Dartmouth’s Quantitative Biomedical Sciences (QBS) doctoral degree is an innovative interdisciplinary program preparing elite professionals to solve complex biomedical challenges. Our students benefit from world-class facilities, renowned faculty, and cutting-edge theory that foster academic and professional excellence.

The application period begins in August and runs through December. All application reviews begin on December 1.

QBS application deadlines

Phd program (fall 2025 start), deadline to apply: december 1, 2024, phd applications will open in august 2024 for fall 2025..

For frequently asked questions about the application process, please refer to the Guarini School of Graduate Studies Application FAQs.

Read more about the admissions requirements for the QBS PhD degree program .

Review the PhD program overview presentation, Fall 2022

Supplemental application materials

In addition to completing the online Dartmouth application, students applying to a QBS program must submit additional supporting materials.

• Describe your motivation and interests in pursuing a graduate degree at Dartmouth, what research interests excite you the most, and how a graduate degree will further your career goals. (500 words max
• Describe your most impactful research experience(s)  and any relevant academic preparation that is not reflected elsewhere in your application. (500 words max)
• The Guarini School is committed to a climate that acknowledges and embraces diversity, supporting a culture that fosters inclusion, and actively pursues equity. Our commitment is driven by a firm belief that welcoming differences of opinion, experience, identity, and perspectives is essential to building a stronger community. We encourage you to share how your perspective on these factors, and your unique experiences within them, will contribute to the diversity of your cohort at Guarini.
• A current curriculum vitae (CV) or resume
• Three letters of recommendation
• Official or unofficial college transcripts for all undergraduate and graduate coursework to the application. Official transcripts will be required should an offer be made to the applicant.
• GRE scores are optional for the current QBS admissions cycle. If you would like your GRE scores considered with your application, please include unofficial score reports with your application. If you chose to submit GRE scores and you are offered admission, official test scores will be required from Educational Testing Services for verification purposes. WES evaluations are acceptable; however, official documents will be required should an offer be made to the applicant.  The institutional code for GRE scores is 3351.
• Official TOEFL exam scores from ETS are required for international students who do not have a degree from an English speaking institution. QBS students are expected to be able to communicate easily and fluently in written and verbal English. Applicants should include unofficial score reports in their application. We accept TOEFL scores of 450 or above for the paper-based exam, internet-based exam scores of 90 or above, as well as IELTS scores with band scores of 7.0 or higher. TOEFL MyBestScores are also accepted if submitted through the ETS system. The institutional code for TOEFL scores is 3351.

Applicants to the PhD program can submit the supplemental application materials to the following address:

Guarini School of Graduate and Advanced Studies Dartmouth College 64 College Street, Suite 6062, Room 102 Hanover, NH 03755

When sending materials such as transcripts, DO NOT send to the Dartmouth College Admissions Office. That office is for applications to the undergraduate college only and doing so may result in delays in processing your application.

Once your application is submitted, you may contact Guarini Admissions to request updates to your application, such as missing transcripts or other elements of the application. Your application will be considered incomplete until all required materials have been received. In order to be eligible for admission consideration, the online application, application fees, and all supporting materials must be received by the QBS team by the specified deadlines for the program.

Information about PhD applications, program requirements, and other questions may be directed to the QBS Program at [email protected] .

Graduate fellowships and scholarships

Financial assistance is available to eligible students applying to the QBS programs. All QBS PhD students receive a fellowship. Learn more about specific details of the QBS Dartmouth Fellowship program .

Visa information for international students

The Office of Visa and Immigration Services at Dartmouth (OVIS) supports the presence and success of international students at Dartmouth. OVIS also provides up-to-date information regarding current SEVIS regulations.

An additional resource for international applicants are the EducationUSA Centers that are located in U.S. embassies and consulates, as well as partner institutions worldwide. We encourage international applicants visit these sites to find additional helpful information about applying to graduate schools and advisories about traveling to the United States.

Contact the QBS team

The QBS team is ready to help you navigate the PhD graduate admissions process. Questions about applications or program requirements can be directed to the QBS Program at: [email protected]

After you apply

What happens between the time you click submit and when you receive a final decision.

Each of us on the admissions committee appreciates how much time candidates put into an application. It shows in your essays and the comments that recommenders make on your behalf.

Your future in the quantitative biomedical sciences is important to us, and we use the application review process to prepare you for the next step of your career. Here's an inside look at how we build a diverse class of quantitative professionals dedicated to improving data-based outcomes around the world.

What we look for in our review

Our admissions committees are made up of a combination of faculty, staff and alumni. Multiple committee members read each application for a thorough initial review. We look at everything you have submitted and take a holistic view of your entire application.

In our review, we make notes about each element in the application.

• Your essay plays an important role in helping us understand your interests and goals and how those relate to the content and objectives of our program. We’re looking for you to draw a path from where you are now to where you want to be in your career. How will our PhD help you?
• How you build relationships with others. This comes through via your recommenders' comments.
• Your academic performance, including trends over time. Do your grades improve from year to year? Are you able to sustain strong academic achievement? Are your standardized test scores (if applicable) consistent with your undergraduate experience?
• If you have any type of research experience, describe your project or role in a project. What skills, methods, and techniques did you learn? Was there anything novel you contributed independently? Discuss your role in listed publications or presentations. Describe how this experienced helped shape your trajectory regarding applying for a PhD or subsequent career and learning goals.

After their initial independent review, admission committee members meet to discuss the application and make a recommendation, write summary comments, and identify any questions they have.

How we evaluate admissions committee recommendations

Our admissions director reviews every recommendation with one to three other admissions committee members in a decision committee meeting. Together, we decide on the next step, which is an interview with the applicant.

Remote interviews with faculty, students, and alumni are held mid-January. Each applicant identifies faculty of interest for the interview. We are purposeful in scheduling each applicant with their choices of self-identified faculty, but we cannot guarantee this will happen 100 percent of the time. Applicants and faculty are paired for individual interviews. Interviews with current students may occur in panel format.

After interviews, the admissions committee meets to review each applicant to make final recommendations for admission. Admissions notifications are sent by mid-February.

How we make a final decision

Before any decision is final, it is reviewed again by the decision committee. As a group, we weigh the benefits of each application, what the candidate may bring to the class, and the current class composition.  We strive to assemble a group of students with different backgrounds who all want to make a positive impact in the quantitative sciences.  We value students with different backgrounds and experiences.

During your time in our program we expect that you will inhabit both learner and teacher roles. As a learner, you will be exposed to novel content in health and healthcare. As a teacher, you will coach and support your peers in areas which you may have deep expertise. Together, you and your peers will experience an intense relationship-based learning opportunity.

We try our best to have a process that is rigorous and fair. We trust the qualitative and quantitative data we collect in the review process. Having every decision go through multiple people helps to ensure the process is thoughtful and thorough.

This page may link to PDF files. Use this link to download Adobe Reader if needed.

Quantitative Biosciences (Ph.D.)

The mission of the Georgia Tech PhD program in Quantitative BioSciences (QBioS) is to enable the discovery of scientific principles underlying the dynamics, structure, and function of living systems. The QBioS program is designed to provide PhD graduates with the skills and expert knowledge necessary to move directly into academia, industry and/or government, where they can apply their specific domain expertise and broadly relevant modeling tools.

	University of Houston
	Jul 01, 2024   2024-2025 Graduate Catalog (Catalog goes into effect at the start of the Fall 2024 semester)     2024-2025 Graduate Catalog (Catalog goes into effect at the start of the Fall 2024 semester) | Education   / Liberal Arts & Social Sciences    > Department of Psychological, Health, and Learning Sciences   / Department of Psychology    > Measurement, Quantitative Methods, and Learning Sciences, PhD The Doctor of Philosophy in Measurement, Quantitative Methods, and Learning Sciences prepares students for employment as faculty members at colleges and universities. Graduates also find employment in areas such as directors of educational components of health care institutions and social service agencies. Typically, these students develop an individually tailored Ph.D. program which emphasizes theory and research in one or more areas related to learning and development, special populations, higher education, health education and/or measurement and statistics. Originally named the Ph.D. Educational Psychology and Individual Differences, the Ph.D. program in Measurement, Quantitative Methods, & Learning Sciences continues to represent core elements of the definition of Educational Psychology, which includes “Instruction in learning theory, human growth and development, and research methods, and psychological evaluations” (according to IPEDS [Integrated Post-secondary Education Data System]), but enhances the employment prospects of program graduates. The MQM-LS degree qualifies students as university and college instructors, program evaluators, researchers in psychological, educational, and community environments, and professionals within various related fields. In addition, it provides them with the skills necessary to fill a variety of roles in other settings in which knowledge of human development, learning theory, research and evaluation methods are essential. Graduates are trained for teaching, research, and leadership careers in academic positions and non-academic settings such as local, state and national agencies that deal with educational policy and practices.  For further information, please see Measurement, Quantitative Methods, and Learning Sciences . Admission Requirements College of Education takes into consideration a number of criteria when determining admission, including prior college or university performance, letters of recommendation, standardized test scores and statement of intent. All applicants must abide by the minimum qualifications for admissions to a masters or doctoral program. All graduate applicants (regardless of citizenship status) must demonstrate proficiency in English to obtain admission to the University. For more information, visit http://www.uh.edu/graduate-school/admissions/international-students/english-proficiency/ . An applicant is responsible for ensuring that all required materials for the evaluation of admissions are received by the College before the program’s deadline. If the application is not complete by the program’s deadline, it will not be evaluated for the admissions. Full details of the online application process can be found at www.uh.edu/graduate-school/admissions/how-to-apply . Applicant checklist: Complete online graduate application including statement of interest, resume/c.v., writing sample, letters of recommendation, and application fee payment. Official transcripts from all previous college/university work sent to the UH Graduate School. Official reporting of GRE scores taken in the last five years International students have additional documentation requirements which can be found at www.uh.edu/graduate-school/admissions/international-students/ Grade Point Average Requirements Admission requirements for the College of Education require a minimum cumulative grade point average (GPA) of 2.6 for undergraduate coursework or over the last 60 credit hours of coursework. The College requires a minimum cumulative grade point average (GPA) of 3.0 for graduate coursework. The College’s admission committees evaluate all credentials submitted by applicants to determine a student’s ability and potential to succeed in graduate study. In addition, the committee is interested in the applicant’s potential to contribute to his/her program of study and the University community as a whole. Please visit the program’s Admission Application Instructions page for more information. Degree Requirements Credit hours required for this degree: 69.0 The curriculum for the MQM-LS Ph.D. program involves the completion of specific coursework that includes foundations of psychological and educational theory, statistics, and research methodology.  This coursework is designed to be consistent with the American Psychological Association’s principles for learner-centered education and with the College of Education’s conceptual model. Completion of the program typically requires four years of full time study, inclusive of coursework, candidacy research project, comprehensive examination portfolio, and dissertation. Courses required for the degree are described below. Department/Foundations Core Courses (21 hours) All students in the MQM-LS doctoral program are required to complete a Program Area Core consisting of seven courses (21 hours).  PHLS 8302 - Research Methods in Psychological and Educational Research Credit Hours: 3.0 PHLS 8319 - Inferential Statistics in Psychological and Educational Research Credit Hours: 3.0 PHLS 8322 - Intermediate Statistical Analysis in Psychological and Educational Research Credit Hours: 3.0 PHLS 8324 - Multivariate Analysis in Psychological and Educational Research Credit Hours: 3.0 PHLS 8300 - Advanced Educational & Psychological Measurement Credit Hours: 3.0 PHLS 8350 - Educational Psychology Credit Hours: 3.0 PHLS 8397 - Selected Topics Credit Hours: 3.0 Psychology of Learning in STEM 3 Program Area Core Requirements (21 hours) All students in the MQM-LS doctoral program are required to complete a Program Area Core consisting of seven courses (21 hours). Three of these courses (9 hours) must be in the area of Learning and Development, and four of these courses (12 hours) must be in the area of Research Methods, Measurement and Statistics. Learning and Development (9 hours total) PHLS 8335 - Sem-Adv Top-Human Development Credit Hours: 3.0 PHLS 8342 - Seminar Learning Theories Credit Hours: 3.0 Educational Disparities and Social Inequality Research Methods, Measurement, and Statistics (12 hours total) Required (6 hours): PHLS 8321 - Structural Equation Modeling in Psychological and Educational Research Credit Hours: 3.0 PHLS 8328 - Hierarchical Linear Modeling in Psychological & Educational Research Credit Hours: 3.0 PHLS 8327 - Longitudinal Data Analysis in Psy/Educ Research Credit Hours: 3.0 PHLS 8397 - Selected Topics     Credit Hours: 3.0 (Topic approved by advisor) SAER 8321 - Survey Mthds in Educ Credit Hours: 3.0 SAER 8370 - Program Eval Research Credit Hours: 3.0 SAER 8320 - Ethnog Mthds Educ Credit Hours: 3.0 CUIN 8377 - Qualitative Inquiry in Education I Credit Hours: 3.0 CUIN 8378 - Qualitative Inquiry in Education II Credit Hours: 3.0 Independent Research Requirements (9 hours minimum) All students in the MQM-LS doctoral program are required to satisfy two major research requirements: the candidacy research paper, and a doctoral dissertation. Both of these projects typically involve the collection, analysis, and interpretation of quantitative or mixed-methods data. PHLS 7398 - Candidacy Research Credit Hours: 3.0 PHLS 8399 - Doctoral Dissertation Credit Hours: 3 Specialization Electives (18 hours minimum) All students in the MQM-LS doctoral program are required to pursue one of two Areas of Specialization: Measurement & Quantitative Methods, or Learning Sciences. For these electives, students are encouraged to pursue coursework pertinent to their individual career goals, including courses offered by faculty within the Department of Psychological, Health, and Learning Sciences, as well as courses offered by the Department of Psychology, and those related to the fields of sociology and other behavioral and social sciences. These electives should be identified in consultation with the student’s academic advisor. A maximum of two (6 hours) independent study courses (e.g., PHLS 8398) can be used to satisfy this requirement. Academic Policies Professional Development Activities  Students in the MQM-LS PhD program are required to satisfy a Professional Development requirement during their first year in the program. Students are required to complete a separate Residency Report for the Fall and Spring semesters of their first year in the program that will serve to satisfy their doctoral residency/professional development requirement. These forms must be approved by the student’s academic advisor, the chair of the department, and the Dean or his/her designee.  The following professional development activities are required for doctoral students in the MQM-LS program. Activities completed each semester should be listed on separate Residency Reports for each semester. Students should consult with their advisor regarding selection of additional activities that will augment their academic preparation in scholarship, teaching, and service, such as attending presentations of scholarly speakers at the University of Houston or elsewhere (e.g., Rice University, the Medical Center, in the community), assisting other doctoral students with data collection, etc.  Attend at least one defense of a candidacy research proposal in Educational Leadership and Policy Studies. Attend at least one defense of a candidacy research final paper in Educational Leadership and Policy Studies.  Attend at least one defense of a dissertation proposal in Educational Leadership and Policy Studies. Attend at least one defense of a dissertation final paper in Educational Leadership and Policy Studies  Attendance at a local, state, or national conference that pertains to education or a relevant social science. The sessions attended may be listed as additional activities.  Attendance at the Houston Symposium for Research in Education, sponsored by the College of Education, when it is offered.   Membership in the Graduate Students Organization Student membership in a professional organization (e.g., American Educational Research Association, American Association for the Study of Higher Education) Candidacy Research Paper MQM-LS doctoral students must complete a candidacy research paper before they are eligible to have their Comprehensive Examination Portfolio submission materials officially reviewed. Students are expected to conduct a research project within the general domain of higher education. The scope of this research project should be equivalent to what would be expected from a master’s level thesis. Students who previously have completed a Master’s Thesis may petition to have the thesis count for the candidacy research requirement and should consult with their academic advisor regarding this matter. College Academic Policies University of Houston Academic Policies     Get the Reddit app A subreddit for the quantitative finance: discussions, resources and research. Getting into the quant industry/considering PhD vs undergrad Hello, I am a math/cs major (rising jun, graduating in 3 yrs) at a target school in the US. Although I am intelligent, I am not a math olympiad or that level of 'cracked'. I would be more than thrilled to work at a SWE role in big tech but I'd rather incorporate math in my job in some way and thus hope to work in quant. I've looked into interview prep for quant—though it is interesting and the questions are akin to problem solving books I do in my free time—I don't want to waste time preparing for interviews IF I have quite a low chance at acceptance as an undergrad considering my lack of spikes. Also, I love learning and would hate to spend less time exploring cool fields (which are still tangentially related to quant shiz) and instead fruitlessly grinding at mental math. Thus, would my time be better spent preparing for PhD applications? Would that improve my chances? Although I enjoy classes and hope to take graduate ones, I'm not sure if I could dedicate myself to a narrow research field for 5/6 years. So should I instead just send it, grind as hard as I can for quant rn? Thanks. If you have any information about transitioning into quant roles without a PhD, please let me know! Biological Sciences Mellon College of Science M.S. in Quantitative Biology and Bioinformatics The study of Biology is undergoing a revolution driven by new technologies that enable scientists to generate extensive amounts of data.  For example, the costs of sequencing nucleic acids have dropped dramatically, resulting in unprecedented amounts of genomic, transcriptomic, and proteomic data.  Advances in imaging extend from the nano to the macro scale to probe function and generate enormous amounts of data that describe behaviours of cells from subcellular to organ-levels.  The new datasets cut across all subdisciplines in biology and enable scientists to ask questions in new ways to reveal the fundamental rules of life. The M.S. in Quantitative Biology and Bioinformatics (MS-QBB) will prepare students for new careers bioinformatics and related fields. Our mission is to provide students who have background in life sciences skills to prepare for careers in bioinformatics. This program allows student to choose a 2-semester or a 3-semester program of study. If you are interested in applying, learn more about the application process on our admissions page or e-mail us . Program Mission To provide students who have a background in biology and other sciences with a practical and focused educational experience to prepare them for careers in bioinformatics and quantitative biological science. 2-semester M.S. in QBB Our 2-semester option allows students to quickly gain the most relevant skills in bioinformatics. Students will begin study in late August and graduate in late May. 3-semester M.S. in QBB - Advanced Study The 3-semester option allows students to spend a third semester gaining additional experience and some more advanced coursework. Students will begin study in late August, have the option to earn course credit with optional summer internships (interested students may apply to these in the first year), then students will complete their third semester in the following Fall and graduate in late December. Students are encouraged to seek external internships after their first year and pursue this degree full-time, completing the program in 3 semesters. Related programs Students who are interested in this program may also want to consider the M.S. in Computational Biology and M.S. in Automated Science programs . Those programs expect a higher level of quantitative background & skills to enter and are designed to engage students with a more in-depth focus computational machine learning competencies and the application of machine learning to biological research. How to Apply The CMU Rales Fellow Program is dedicated to developing a diverse community of STEM leaders from underrepresented and underresourced backgrounds by eliminating cost as a barrier to education. Learn more about this program for master's and Ph.D. students. Learn more Join our growing network of prospective students! Fill in the form below to connect with a program advisor Graduate Students Administration Exam Prep > Prepare for Business School > Business School & Careers > Explore Programs > Connect with Schools > How to Apply > Help Center > About the Exam Register for the Exam Plan for Exam Day Prep for the Exam About the Executive Assessment Register for the Executive Assessment Plan for Assessment Day Prepare for the Assessment NMAT by GMAC Shop GMAT™ Official Prep About GMAT™ Official Prep Prep Strategies Personalized Prep Plan GMAT Mini Quiz Executive Assessment Exam Prep NMAT by GMAC Exam Prep Prepare For Business School Business Fundamentals Skills Insight Business School & Careers Why Business School Student Experience Business Internships B-School Go Quiz: Are You Leadership Material? MBA Return on Investment (ROI) Calculator Estimate Your Salary Success Stories Diversity and Inclusion Women in Business Explore Programs Top Business School Programs Quiz: Which Post Graduate Program is Right for You? Quiz: Find the Best Program for Your Personality Business School Rankings Business Master's Programs MBA Programs Study Destinations Find Programs Near Me Find MBA Programs Find Master's Programs Find Executive Programs Find Online Programs Connect with Schools About GradSelect Create a GradSelect Profile Prep Yourself for B-School Quiz: Can You Network Like An MBA? Events Calendar School Events GMAC Tours Events In-Person Events Online Events How to Apply Apply to Programs The Value of Assessments Admissions Essays Letters of Recommendation Admissions Interviews Scholarships and Financing Quiz: What's Your Ideal Learning Style? Help Center Understanding your score, quick links. After receiving your Official Score Report , you may be tempted to focus on your Total Score. However, it will be just as important to review your percentile ranking as you look to understand your results. The GMAT Score Scale If you're familiar with the  previous edition  of the GMAT, the GMAT Exam (10th Edition), you'll notice the Total Score scale is different from the current edition, the GMAT Exam (Focus Edition). This change has been made to ensure you  and  schools can easily distinguish scores between editions. Total Score GMAT Exam (Focus Edition): 205–805 GMAT Exam (10th Edition):  200–800 The score scale for the GMAT Exam (Focus Edition) has also been adjusted to reflect changes in the test-taking population, which has become more diverse and global. Over the years, scores have shifted significantly, resulting in an uneven distribution. The updated score scale fixes that, thus allowing schools to better differentiate your performance on the exam. 645 is the New 700 On the GMAT Exam (10th Edition), many test takers aimed for a score of 700. On the GMAT Exam (Focus Edition), a score of 645 is equivalent to a 700 due to the new score scale. Since they are both in the 89th percentile, they represent the same level of performance. Therefore, while scores may look "lower" in comparison, they aren't. The GMAT Exam (Focus Edition) is scored differently, and business schools know this when reviewing your application and paying more attention to your percentile ranking. Percentile Rankings What are Percentile Rankings? Percentile rankings indicate what percentage of test takers you performed better than. For example, a percentile ranking of 75% means that you performed better than 75% of other test takers, and 25% of test takers performed better than you. Interpreting Your Percentile Ranking Total Score  Percentile Total Scores for the GMAT Exam (Focus Edition) range from 205 to 805. Your GMAT Total Score is composed of the Quantitative Reasoning, Verbal Reasoning, and Data Insights sections of the exam. The contribution of each section score to Total Score is equally weighted across sections. Score Concordance The GMAT Total Score ranges from 205 to 805 on the current edition of the exam, the GMAT Exam (Focus Edition), while the previous edition of the GMAT, the GMAT Exam (10th Edition) has a Total Score range of 200-800. Because the GMAT Exam (Focus Edition) Total Score scale AND score scale distribution are different from the GMAT Exam (10th Edition), comparing total scores or section scores from the previous version of the exam to the current edition of the GMAT is not appropriate, accurate, or a meaningful comparison of performance. If your relative competitiveness based on the GMAT Exam (Focus Edition) needs to be compared to the GMAT Exam (10th Edition), it is more appropriate to compare percentile rankings rather than comparing total scores.  U se the concordance table below to show score distributions between the two versions of the exam by percentile (updated January 2024). 175 IMAGES Quantum University has opened PhD admissions for January 2021 Quantum Mechanics PhD Programs: 4-5 Years To An Advanced Degree Quants New Quantum Science and Engineering Ph.D. program provides training in Quantum Science: Ph.D. Degree Quantum Science and Engineering VIDEO Quant HACKS- Shortcut to solve replacement questions in 1 minute (Episode 6) Why No Stats Majors in Quant? How to choose the RIGHT Master of Financial Engineering program| My own experience & tips #58 Is the FRM Worth It for Quants? Project #17 NLP COMMENTS PhD Programs with Quantitative Concentrations Doctoral Programs in Quantitative Methods Business Administration Econometrics and Statistics , one of the eight dissertation areas in the Booth School PhD program, is concerned with the combination of economic, mathematical, and computer techniques in the analysis of economic and business problems such as forecasting, demand and cost analyses ... Ph.D. in Quantitative Methods A doctoral program focused on measurement and evaluation that trains students to create new research methodologies and design empirical data analyses. The Quantitative Methods Ph.D. program is designed to prepare future professors at research universities and principal investigators at research and assessment organizations in education ... Quantitative Finance Quantitative Finance MS and PhD. The Stony Brook Department of Applied Mathematics and Statistics offers MS and PhD STEM designated training in quantitative finance. Summary of QF program for potential students is available at QF chair webpage. Because of the strong demand, admission is highly competitive at both the MS and PhD levels in ... Quantitative Methods, PhD < University of Pennsylvania 2024-25 Catalog. Quantitative Methods, PhD. The Ph.D. program in Quantitative Methods is designed to prepare students for faculty positions at universities and important responsibilities at research and assessment organizations. Graduates will be prepared to design first rate empirical research and data analyses and to contribute to development ... Quantitative Methods Doctoral applicants admitted to the QM program receive a guaranteed 5 years of stipend and tuition support, which usually takes the form of a combination of research assistantships and/or teaching assistantships in quantitative courses (for instance, the introductory graduate statistics sequence). PhD Program Wharton's PhD program in Finance provides students with a solid foundation in the theoretical and empirical tools of modern finance, drawing heavily on the discipline of economics. ... engineering, statistics, and other quantitative disciplines have an ideal background for doctoral studies in this field. Effective 2023, The Wharton Finance ... Quantitative Finance Specialization The Quantitative Finance specialization in the Ph.D. in Management Science and Analytics program is excellent preparation for either academic careers or for students who want to apply the theoretical, analytical, and quantitative rigor of management science to careers in finance. Dissertation research in this area may include a wide range of ... Why Study for a Mathematical Finance PhD? A good mathematical finance PhD program will make extensive use of your undergraduate knowledge and put you through graduate level courses on stochastic analysis, statistical theory and financial engineering. It will also allow you to take courses on general finance, particularly on corporate finance and derivative securities. Psychology (Quantitative Research Methods), PHD The PhD program in psychology with a concentration in quantitative research methods offers an immersive education in advanced statistical techniques and research methodologies that are employed in the conduct of both basic and applied psychological research. A collaborative, interdisciplinary approach to research empowers students to deepen ... PhD in Mathematical Finance » Academics The PhD in Mathematical Finance is for students seeking careers in research and academia. Doctoral candidates will have a strong affinity for quantitative reasoning and the ability to connect advanced mathematical theories with real-world phenomena. They will have an interest in the creation of complex models and financial instruments as well ... Quantitative and Computational Biology The Program in Quantitative and Computational Biology (QCB) is intended to facilitate graduate education at Princeton at the interface of biology and the more quantitative sciences and computation. Administered from The Lewis-Sigler Institute for Integrative Genomics, QCB is a collaboration in multidisciplinary graduate education among faculty ... Measurement, Quantitative Methods, & Learning Sciences Doctoral Program The University of Houston's Measurement, Quantitative Methods, & Learning Sciences (MQM-LS) doctoral program equips students with the skills necessary to design, conduct and interpret quantitative research projects that help solve our society's most difficult problems. Students develop a broad understanding of psychological and learning theories while also receiving strong quantitative methods ... Quantitative Methods Doctoral Program Quantitative Methods doctoral students are required to complete: EDP Foundation courses, QM Program courses, Out-of-Specialization courses, and. Qualifying Process and Dissertation coursework. Student coursework may vary depending on prior graduate coursework and waivers. All required courses must be completed with a grade of at least B-. PhD Program in Finance 2023-24 Curriculum Outline. The MIT Sloan Finance Group offers a doctoral program specialization in Finance for students interested in research careers in academic finance. The requirements of the program may be loosely divided into five categories: coursework, the Finance Seminar, the general examination, the research paper, and the dissertation. PDF PHD PROGRAMS QUANTITATIVE METHODS Meet Online with an Admissions Specialist. purdue.university/phd-meet. PHD PROGRAMS. Feb 2023. QUANTITATIVE METHODS. PLACEMENT. PROGRAM REQUIREMENTS. • Complete required coursework • Serve as teaching or research assistant • Complete 2 research papers • Pass preliminary examination • Write and defend dissertation Quantitative Methods ... Quantitative Methods Home Quantitative Methods Home. Program Details. Research. Funding. In an era of dwindling resources and increasing competition, optimization questions have assumed a new and urgent importance. To that end, doctoral seminars in Quantitative Methods focus on advanced optimization applications and methodologies. Related courses are available from ... 2024 QuantNet Ranking of Best Financial Engineering Programs Baruch College. New York, NY Master of Financial Engineering. 1. Total Score. 100. Peer Score. 4.3. The Baruch College's Master of Financial Engineering (MFE) program is offered by the Math Department under the Weissman School of Arts and Sciences. This three-semester program starts in the Fall semester. Quantitative Analytics Program The rotational Quantitative Analytics program is designed to provide you with the opportunity to gain comprehensive professional and industry experience that prepares you to develop, implement, calibrate, and validate various analytical models. Wells Fargo hires a number of PhDs and Master's Candidates within the Capital Markets, and Risk ... Quantitative Finance Programs Markets - Quantitative Research : Develop and maintain sophisticated mathematical models, cutting-edge methodologies, and infrastructure to value and hedge financial transactions ranging from vanilla flow products to high and low-frequency trading algorithms. Model Risk Governance & Review: Work with model developers and the business to ... APPLY for QBS PhD Information about PhD applications, program requirements, and other questions may be directed to the QBS Program at [email protected]. Graduate fellowships and scholarships. Financial assistance is available to eligible students applying to the QBS programs. All QBS PhD students receive a fellowship. Quantitative Biosciences (Ph.D.) The mission of the Georgia Tech PhD program in Quantitative BioSciences (QBioS) is to enable the discovery of scientific principles underlying the dynamics, structure, and function of living systems. The QBioS program is designed to provide PhD graduates with the skills and expert knowledge necessary to move directly into academia, industry and/or government, where they can apply their ... MSCF MSCF Student & Faculty Portal. Pittsburgh Location (412) 268- 3629. New York City Location (412) 268-8446. MSCF Admissions (412) 268-3679 [email protected]. Discover the unique advantages of Carnegie Mellon's top-ranked MSCF program and learn about quantitative finance career opportunities. Program: Measurement, Quantitative Methods, and Learning Sciences, PhD All students in the MQM-LS doctoral program are required to complete a Program Area Core consisting of seven courses (21 hours). Three of these courses (9 hours) must be in the area of Learning and Development, and four of these courses (12 hours) must be in the area of Research Methods, Measurement and Statistics. Getting into the quant industry/considering PhD vs undergrad A subreddit for the quantitative finance: discussions, resources and research. ... If you can get into a PhD program (a 5-6 year funded program) you can get into a funded masters program (2 years) at the same caliber of institution. Getting into a PhD program is extremely difficult compared to MFE/MS programs. Quantitative Principles for Clinical Research This certificate program addresses the needs of residents and fellows to attain knowledge in the basic principles of clinical research — analyzing data, understanding medical literature, and communicating results. All coursework is online, providing flexibility for the trainees and training programs. M.S. in Quantitative Biology and Bioinformatics The M.S. in Quantitative Biology and Bioinformatics (MS-QBB) will prepare students for new careers bioinformatics and related fields. Our mission is to provide students who have background in life sciences skills to prepare for careers in bioinformatics. This program allows student to choose a 2-semester or a 3-semester program of study. Understanding Your Score Your GMAT Total Score is composed of the Quantitative Reasoning, Verbal Reasoning, and Data Insights sections of the exam. The contribution of each section score to Total Score is equally weighted across sections. ... Brought to you by GMAC, the global mission-driven organization of leading graduate business schools. ©2002-2024, Graduate ... essay homework paper phd project resume review speech study thesis