phd for quant research

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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.

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You May Be Interested In

Related programs.

• Education Policy M.S.Ed. 
• Education, Culture, and Society Ph.D. 
• Higher Education Ph.D. 
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How To Get A Quant Job Once You Have A PhD

In this article we are going to discuss an issue that repeatedly crops up via the QuantStart mailbox, namely how to get a quant job once you have a PhD . There's a lot of confusion around this topic because quite a few people who currently work in academia and want to make the shift believe that it is quite straightforward to "walk into" a high-paying financial role. While this may have been true 10-15 years ago, the reality of the current job market is such that quant roles are now highly competitive and candidates need to stand out if they are to get the best jobs.

Firstly we'll discuss what sort of candidates you will be competing against when considering going for interview. Secondly, we'll discuss how to make an honest assessment of your PhD and what you got out of it that might be relevant to quantitative finance roles. Finally, we'll consider whether it is necessary to return to school in order to train up in a quant-specific qualification.

The Competition

I've made it rather clear on QuantStart that the competition for some of the top quantitative trading researcher roles can be extremely tough. In the UK the best roles tend to be filled well upstream of any "front door" interview process. Usually extremely bright academics in mathematics, physics, computer science, economics or mathematical finance are head-hunted for a particular skill set, such as deep expertise on market microstructure, insight into high-frequency trading algorithms, novel stochastic calculus techniques for certain derivatives pricing regimes or extensive statistical machine learning knowledge that applies to datasets used by such funds.

When such quant researcher roles ARE opened up to the public they will often state that they are looking for "only the best and brightest", which in the UK usually means "Top Five" universities (Cambridge, Oxford, Imperial College, LSE and UCL). In the US this will mean high-end Ivy League institutions. The adverts will often state that they want to see evidence of consistent Mathematical Olympiad prizes and an extensive publication list in a relevant field.

While this is certainly true of the top roles, there are plenty of other (very well paying and prestigious) jobs that also need filling. Bear in mind that there are only so many Mathematical Olympiad winners, after all! Thus one should not be disheartened when seeing numerous adverts asking for such qualifications. There are plenty of smaller funds and boutique outfits that do not have the resources to aggressively hunt for the ultimate talent and so will be more than willing to employ bright PhDs who might not necessarily have an Olympiad track record.

Honestly Assess Your PhD

The first task to carry out when applying for quant roles is an honest assessment of your PhD and what you achieved with it . Primarily you need to consider the level of mathematical ability you were able to attain as well as your computational programming skill.

Quant roles in the derivative pricing space, known traditionally as the "quant analyst" or "financial engineer", require a reasonable amount of mathematical sophistication. Specifically, expertise in stochastic calculus, probability and measure theory. These are topics usually taught in an undergraduate mathematics course, but can form a component of taught graduate school PhDs. In addition they require a good understanding of scientific programming usually in C++, Python or MatLab. Since the role of a quant analyst is often to code up an implementation of a particular algorithm from a research paper, under heavy deadlines, it is quite naturally suited to those with PhDs of this type.

Quant roles in the algorithmic trading and quant hedge fund world are almost exclusively going to require novel methods for generating "alpha" (i.e. excess return above a benchmark). Usually this is accomplished via time series analysis and econometrics, but more recently statistical machine learning techniques have been applied, as have methods related to sentiment analysis. Some of the best quant funds make extensive use of even more advanced graduate level mathematics in the realms of algebraic geometry, number theory and information theory. Hence anything highly mathematically, statistically or physically oriented is likely to be of interest to a top quant hedge fund.

As for computer scientists and strong scientific software developers, generally there is always work available for quantitative developer roles. Although you will be competing against those with industry experience in rigourous software engineering. Hence "academic code" of the "20,000 line single-file of Fortran" variety might be a bit of a hindrance! Make sure to brush up on the more modern software development methodologies such as OOP , Agile , etc.

I want to discuss specific PhD fields as well, to give you an idea of where you might consider focusing your efforts based on what you have previously studied:

• Pure Mathematics - The top funds generally hire the pure mathematicians from esoteric realms such as algebraic geometry and information theory. Banks will also take individuals who study stochastic calculus to a high level for their derivatives research teams.
• Mathematical Finance - Portfolio optimisation and derivatives pricing are two common themes studied in mathematical finance PhDs. You will often have collaborated with banks during your PhD, so it is unlikely your job prospects will be slim! If you are struggling, it can be very helpful to contact department heads as they will often have a strong network.
• Theoretical Physics - Funds will be very interested in your ability to model physical phenomena, either through direct or statistical approaches. Some theoretical physics areas are highly mathematical (Cosmology, String Theory, Quantum Field Theory etc) and so the advice given to theoretical physics PhDs is similar to pure mathematicians.
• Computational Physics/Engineering - The main skillset taught here is how to take an algorithm and produce a robust scientific computing implementation, perhaps in a parallelised fashion. This is an extremely useful skill for quant work both in banks and funds, especially for developing infrastructure. Make sure however to brush up on core topics such as statistics and stochastic calculus prior to interview.
• Statistics/Econometrics - Statisticians and theoretical econometricians will be in good demand from technical quant funds, especially in the Commodity Trading Advisor (CTA)/Managed Futures space. The time series modelling will be highly appropriate here.
• Computer Science/Machine Learning - Many funds are now making extensive use of machine learning and optimisation tools, which are the natural domain of the theoretical computer scientist and, more recently, the "data scientist". Familiarity with statistical machine learning and Bayesian methods will be highly attractive.
• Bioinformatics - Bioinformaticians also make extensive use of machine learning tools on "big data" sets. For interview you will want to emphasise your familiarity with such tools and your programming capability. Depending upon your background you may need to brush up on your (pure) mathematics for interview questions.
• Economics/Finance - Economics and Finance PhDs do not always teach you the mathematical maturity necessary for pure quant work, but it really depends on the project. You will need to be honest with yourself about where you lie on the mathematical spectrum. In addition you will need to consider your programming ability.

Heading Back To School

An extremely common question that I receive in the QuantStart mailbag is whether to return to school for finance-specific training subsequent to a PhD.

I've previously documented my views on Masters in Financial Engineering (MFE) programs as related to quantitative trading . In essence I believe that MFEs are not hugely suitable for quantitative trading research work, but they are a good entry point into investment banking quant work.

If your PhD was not heavy on quantitative or programming work, but you have a sufficiently mature mathematical background, then it can make good sense to take a MFE assuming that you can afford to fund the course. A MFE at a top-tier school will provide you with a solid network of other candidates (and thus people who might later help you secure a role), a relatively healthy recruitment position upon graduation and a useful skillset for investment banking derivatives pricing work.

I would advise against returning to school if you have a strong quantitative PhD as you simply won't need the additional qualifications and you should be able to pick up the necessary interview material yourself, albeit with a lot of study.

If you have a PhD in a non-quantitative field and your background is not sufficiently mathematical, then you should definitely consider that you will likely need to return to school if you truly want to work in quantitative finance. In particular you will need to study an undergraduate degree that has a strong quantitative component such as Mathematics or Physics as these two degrees will generally let you transition into other quantitative fields easily.

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Do you need a PhD for a quant job in finance? In the pandemic, the answer is yes

As demand for quants to work in banks and hedge funds increased in recent years, so the number of Masters courses in quantitative finance proliferated wildly. A year into the COVID pandemic, however, it seems the quant Masters boom may be turning into the quant masters bust. 

Headhunters in London and New York say they are awash in applications from juniors with masters and bachelors degrees looking for new quant roles. Few places want to hire them.

"Banks and hedge funds want individuals that they can trust to work from home in a productive capacity," says Ben Hodzic, head of quant recruitment at Selby Jennings in New York. "There's a lot of hesitation when it comes to hiring graduates or junior folks who might need hand-holding and who aren't familiar with the data.

"The real appetite now is for people with five to seven years experience who can hit the ground running, pull the data and produce a hypothesis," he adds. "There's big demand for those sorts of profiles."

Although they might seem junior, Hodzic said candidates with quantitative PhDs are actually perceived as having real experience. "You'd think a PhD graduate would fall into an entry-level bucket, but if you're a PhD from a top tier university with years of research experience under your belt, you're usually joining at a mid to senior level and are trusted to work well on your own." 

It's graduates and masters candidates who are out in the cold, he adds.

The situation is arguably worse in London, where one headhunter says there are additional junior quants on the street after quant hedge funds like Winton Capital Management made cuts last year (Winton reduced headcount from 296 to 230 people in 2019 and is thought to have made further cuts last November). "Juniors are finding it hard now," she says. "There's far more hiring for senior quants, portfolio managers and sub-portfolio managers." Accordingly, Winton just hired John Ward, a former portfolio manager from Green Park Research, as a PM.

One place that's likely to make additional quant hires soon is the Abu Dhabi Investment Authority (ADIA). In December, it hired Thomas Schmelzer, the former head of quant research at Lobnek Wealth Management in Geneva as its quant research and development lead. 

Schmelzer has a D Phil in mathematics and numerical analysis from Oxford University. Aspiring quants may want to get similarly qualified: "A PhD is a foolproof way to work on your research without having to rely on a company," says Hodzic, "It will make you a far more marketable candidate." 

And if you don't have the time, money, or appetite for a quant PhD? Hodzic advises that you look for data roles in fintech firms and at technology vendors as a starting point. "If you've worked on different data analytics projects with varying data streams a hedge fund will at least see that it won't need to do too much handholding if it hires you," he says.  

Have a confidential story, tip, or comment you’d like to share? Contact:  [email protected]  in the first instance. Whatsapp/Signal/Telegram also available.

Bear with us if you leave a comment at the bottom of this article: all our comments are moderated by human beings. Sometimes these humans might be asleep, or away from their desks, so it may take a while for your comment to appear. Eventually it will – unless it’s offensive or libelous (in which case it won’t.)

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And it was already hard enough. 😂

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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.

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School of Education

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Ph.D. in Qualitative and Quantitative Research Methodology

Qualitative and quantitative research methodology, (formerly ph.d. in inquiry methodology).

This unique program enables students to focus on quantitative research, qualitative research, or an integrated program of study.

The flexible curriculum enables you to delve deeply into your chosen area of interest, from statistical modeling to ethnography, from discourse and narrative analysis to psychometrics and assessment.

Yet our program is rigorous enough to ensure that all graduates are able to meaningfully contribute to the study of social and behavioral research.

Application Deadlines

Admission Requirements

The Graduate Studies Office will accept unofficial transcripts and self-reported test scores for admission reviews. Any admission made with these documents would be conditioned on receipt of official documents, which should be provided as soon as possible.

If you are currently enrolled or have applied in the past year, you are eligible for a reduced application fee of $35. Learn more »

• Bachelor’s degree from an accredited institution
• Minimum undergraduate GPA of 2.75 out of 4.00
• Personal statement
• Resume (required from international students only)
• Three letters of recommendation
• Minimum 79 TOEFL score or minimum 6.5 IELTS score or minimum 115 Duolingo score (international students only)

Learn more about how to apply

Program Requirements

• Ph.D. in Qualitative and Quantitative Research Methodology – (formerly Ph.D. in Inquiry Methodology) Requirements

Costs listed are per credit hour.

2023-2024 Academic Year

2024-2025 Academic Year

*Does not include all fees, which will vary depending on the number of credits enrolled. Find more information and calculate your expected costs at Student Central .

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Qualifying Examination

At the completion of course work and before the dissertation, doctoral students specializing in Inquiry Methodology will need to pass a qualifying exam in the form of portfolio of work and an oral examination. This examination is tailored to the student's program of study. All students with a minor in education must also take a minor qualifying examination. Some departments outside of the School of Education waive the minor qualifying examination, under certain conditions.

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As a student you will have the opportunity to focus on methodology through theory and practice that cuts across a divide in qualitative and quantitative methods.

We are dedicated to advancing the understanding of social inquiry, especially with respect to the field of education, and we imagine these possibilities to be necessarily inclusive of methods typically disenfranchised from one another.

This 90-credit hour degree program requires students to spend at least two consecutive semesters on campus. Up to 30 credit hours may be transferred from another institution.

A 12-credit hour minor is also available to doctoral students majoring in other disciplines.

David Rutkowski ED 4234 drutkows@iu.edu (812) 856-8384

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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.

• 84 credit hours, a written comprehensive exam, an oral comprehensive exam, a prospectus and a dissertation

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

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Rigorous, discipline-based research is the hallmark of the MIT Sloan PhD Program. The program is committed to educating scholars who will lead in their fields of research—those with outstanding intellectual skills who will carry forward productive research on the complex organizational, financial, and technological issues that characterize an increasingly competitive and challenging business world.

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PhD studies at MIT Sloan are intense and individual in nature, demanding a great deal of time, initiative, and discipline from every candidate. But the rewards of such rigor are tremendous:  MIT Sloan PhD graduates go on to teach and conduct research at the world's most prestigious universities.

PhD Program curriculum at MIT Sloan is organized under the following three academic areas: Behavior & Policy Sciences; Economics, Finance & Accounting; and Management Science. Our nine research groups correspond with one of the academic areas, as noted below.

MIT Sloan PhD Research Groups

Behavioral & policy sciences.

Economic Sociology

Institute for Work & Employment Research

Organization Studies

Technological Innovation, Entrepreneurship & Strategic Management

Economics, Finance & Accounting

Accounting  

Management Science

Information Technology

System Dynamics  

Those interested in a PhD in Operations Research should visit the Operations Research Center .  

PhD Program Structure

Additional information including coursework and thesis requirements.

MIT Sloan Predoctoral Opportunities

MIT Sloan is eager to provide a diverse group of talented students with early-career exposure to research techniques as well as support in considering research career paths.

Rising Scholars Conference

The fourth annual Rising Scholars Conference on October 25 and 26 gathers diverse PhD students from across the country to present their research.

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The goal of the MIT Sloan PhD Program's admissions process is to select a small number of people who are most likely to successfully complete our rigorous and demanding program and then thrive in academic research careers. The admission selection process is highly competitive; we aim for a class size of nineteen students, admitted from a pool of hundreds of applicants.

What We Seek

• Outstanding intellectual ability
• Excellent academic records
• Previous work in disciplines related to the intended area of concentration
• Strong commitment to a career in research

MIT Sloan PhD Program Admissions Requirements Common Questions

Dates and Deadlines

Admissions for 2024 is closed. The next opportunity to apply will be for 2025 admission. The 2025 application will open in September 2024. 

More information on program requirements and application components

Students in good academic standing in our program receive a funding package that includes tuition, medical insurance, and a fellowship stipend and/or TA/RA salary. We also provide a new laptop computer and a conference travel/research budget.

Funding Information

Throughout the year, we organize events that give you a chance to learn more about the program and determine if a PhD in Management is right for you.

PhD Program Events

August phd program overview.

During this webinar, you will hear from the PhD Program team and have the chance to ask questions about the application and admissions process.

Discover Your Doctoral Path

An event for prospective students with Boston-area management programs

September 12 PhD Program Overview

September 25 phd program overview.

Complete PhD Admissions Event Calendar

Unlike formulaic approaches to training scholars, the PhD Program at MIT Sloan allows students to choose their own adventure and develop a unique scholarly identity. This can be daunting, but students are given a wide range of support along the way - most notably having access to world class faculty and coursework both at MIT and in the broader academic community around Boston.

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Profiles of our current students

MIT Sloan produces top-notch PhDs in management. Immersed in MIT Sloan's distinctive culture, upcoming graduates are poised to innovate in management research and education.

Academic Job Market

Doctoral candidates on the current academic market

Academic Placements

Graduates of the MIT Sloan PhD Program are researching and teaching at top schools around the world.

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MIT Sloan Experience

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The PhD Program is integral to the research of MIT Sloan's world-class faculty. With a reputation as risk-takers who are unafraid to embrace the unconventional, they are engaged in exciting disciplinary and interdisciplinary research that often includes PhD students as key team members.

Research centers across MIT Sloan and MIT provide a rich setting for collaboration and exploration. In addition to exposure to the faculty, PhD students also learn from one another in a creative, supportive research community.

Throughout MIT Sloan's history, our professors have devised theories and fields of study that have had a profound impact on management theory and practice.

From Douglas McGregor's Theory X/Theory Y distinction to Nobel-recognized breakthroughs in finance by Franco Modigliani and in option pricing by Robert Merton and Myron Scholes, MIT Sloan's faculty have been unmatched innovators.

This legacy of innovative thinking and dedication to research impacts every faculty member and filters down to the students who work beside them.

Faculty Links

• Accounting Faculty
• Economic Sociology Faculty
• Finance Faculty
• Information Technology Faculty
• Institute for Work and Employment Research (IWER) Faculty
• Marketing Faculty
• Organization Studies Faculty
• System Dynamics Faculty
• Technological Innovation, Entrepreneurship, and Strategic Management (TIES) Faculty

Student Research

“MIT Sloan PhD training is a transformative experience. The heart of the process is the student’s transition from being a consumer of knowledge to being a producer of knowledge. This involves learning to ask precise, tractable questions and addressing them with creativity and rigor. Hard work is required, but the reward is the incomparable exhilaration one feels from having solved a puzzle that had bedeviled the sharpest minds in the world!” -Ezra Zuckerman Sivan Alvin J. Siteman (1948) Professor of Entrepreneurship

Sample Dissertation Abstracts - These sample Dissertation Abstracts provide examples of the work that our students have chosen to study while in the MIT Sloan PhD Program.

We believe that our doctoral program is the heart of MIT Sloan's research community and that it develops some of the best management researchers in the world. At our annual Doctoral Research Forum, we celebrate the great research that our doctoral students do, and the research community that supports that development process.

The videos of their presentations below showcase the work of our students and will give you insight into the topics they choose to research in the program.

Attention To Retention: The Informativeness of Insiders’ Decision to Retain Shares

2024 PhD Doctoral Research Forum Winner - Gabriel Voelcker

Watch more MIT Sloan PhD Program  Doctoral Forum Videos

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PhD in Educational Studies, Quantitative Research, Evaluation and Measurement

The Quantitative Research, Evaluation and Measurement (QREM) program prepares you to become an expert in research design and statistics, program evaluation, and applied measurement and testing. These skills are an essential component as you seek careers in education, government or business settings. This applied program teaches you the critical skills needed for educational inquiry and quantitative techniques. Students in the program need strong analytical backgrounds, critical thinking, and communication and writing skills. 

Program features 

The faculty keeps you the focus of the QREM program, allowing you to make the program your own. 

The tight-knit QREM community lets you get to know faculty and student colleagues on and off campus. 

An active student organization that includes members from all over the world.

Teaching opportunities for graduate students.

Career Paths 

Funding Options 

Key to teaching, research and learning in the college, associateships provide students with professional experience and financial support. 

These financial awards are made by Ohio State to students based on academic merit through a university-wide competition. 

The college annually awards scholarships to its students to support their academic goals. 

Degree Requirements

Minimum program hours

credit hours total

Deadline to apply

Program start

Autumn Semester

View Quantitative Research, Evaluation, and Measurement (EDUCST-PH, QREM) Curriculum Sheet

Additional Application Requirements

Academic Writing Sample

Special Statement of Intent

• Why are you interested in studying Quantitative Research, Evaluation, and Measurement? Why are you interested in the QREM doctoral program at Ohio State more specifically?
• Describe any experience you have with educational, psychological, or other research. What was the nature of the research? What was your involvement?
• Explain how the Ph.D. in QREM fits into your long-term professional goals. At this point, what type of goals or career do you plan to pursue after earning your doctoral degree?
• What faculty member(s) associated with the OSU QREM program do you see as a good fit for you as a mentor?

Apply to Ohio State

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Psychology (Quantitative Research Methods), PhD

• Program description
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• Tuition information
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Data Analysis, Data Analytics, Data Mining, Data Science, Data analysis and mining, Machine Learning, Psychology, Quantitative Science, Research Methods, analysis, approved for STEM-OPT extension, statistics

Elevate your expertise in psychology research methodologies at one of the top-ranked programs in the nation. You'll collaborate with esteemed faculty to master cutting-edge statistical techniques, innovate in psychological research and become a leader in shaping the future of the field.

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.

This program may be eligible for an Optional Practical Training extension for up to 24 months. This OPT work authorization term may help international students gain skills and experience in the U.S. Those interested in an OPT extension should review ASU degrees that qualify for the STEM-OPT extension at ASU's International Students and Scholars Center website.

The OPT extension only applies to students on an F-1 visa and does not apply to students completing a degree through ASU Online.

• College/school: The College of Liberal Arts and Sciences
• Location: Tempe

84 credit hours, a written comprehensive exam, an oral comprehensive exam, a prospectus and a dissertation

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.

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.

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

Department of Psychology | PSY 201 [email protected] 480-727-4561

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.

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Quants: The Rocket Scientists of Wall Street

Getty Images, Prapass Pulsub

Quantitative analysts are professionals who understand the complex mathematical models that price financial securities and are able to enhance them to generate profits and reduce risk. As financial securities have become increasingly complex, demand has grown steadily for quantitative analysts , often called simply "quants," or even the colloquially affectionate "quant geeks."

Because of the challenging nature of the work—which needs to blend mathematics, finance, and computer skills effectively—quant analysts are in great demand and able to command very high salaries. Here's a look at what they do, where they work, how much they earn, and what knowledge is required, to help you decide whether this may be the career for you.  

Key Takeaways

• Quantitative analysts, or quants, combine their skills in finance, math, and computer software to analyze and predict markets, creating complex models that can be used to price and trade securities.
• They tend to work in investment banks and hedge funds, although insurance companies, commercial banks, and financial software and information providers may also hire them.
• Quants work in major financial centers in the U.S. and in London and Asia, among other places across the globe.
• Firms often look for candidates who have a master's degree or a Ph.D. in a quantitative subject, such as mathematics, economics, finance, or statistics.
• Compensation can be in the low-to-middle six figures.

What Do Quantitative Analysts Do?

Quantitative analysts design and implement complex models that allow financial firms to price and trade securities. They are employed primarily by investment banks and hedge funds, but sometimes also by commercial banks, insurance companies, and management consultancies; in addition to financial software and information providers.

Quants who work directly with traders, providing them with pricing or trading tools, are often referred to as " front-office " quants.

In the " back office ," quants validate the models, conduct research, and create new trading strategies. For banks and insurance companies, the work is focused more on risk management than trading strategies. Front-office positions are typically more stressful and demanding but are better compensated.

The high demand for quants is driven by multiple trends:

• The rapid growth of hedge funds and automated trading systems
• The increasing complexity of both liquid and illiquid securities
• The need to give traders, accountants, and sales reps access to pricing and risk models
• The ongoing search for market-neutral investment strategies 

Quantitative analyst positions are found almost exclusively in major financial centers with trading operations. In the United States, that would be New York and Chicago, and areas where hedge funds tend to cluster, such as Boston, Massachusetts, and Stamford, Connecticut.

Across the Atlantic, London dominates; in Asia, many quants are working in Hong Kong, Singapore, Tokyo, and Sydney, among other regional financial centers.

Despite the heavy concentration in those cities, quants are found all over the world—after all, many global firms analyze and/or trade complex securities, which creates demand for the quant's brainpower and abilities.

But the problem that a quant working in Houston or San Francisco faces is that changing employers most likely would mean changing cities, whereas a quant working in Manhattan should be able to interview for and find a job within a mile or two of their previous one. 

What Do Quants Earn?

Compensation in the field of finance tends to be very high, and quantitative analysis follows this trend. It is not uncommon to find positions with posted salaries of $200,000 or more, and when you add in bonuses, a quant could earn over $300,000 per year.

As with most careers, the key to landing high-paying jobs is a resume filled with experience, including with well-known employers, as well as reliance on recruiting firms and professional networking for opportunities. 

The highest-paid positions are with hedge funds or other trading firms, and part of the compensation depends on the firm's earnings, also known as profit and loss (P&L) .

At the other end of the pay scale, an entry-level quant position may earn only $120,000 to $210,000, but this type of position provides a fast learning curve and plenty of room for future growth in both responsibilities and salary.

Also, some of the lower-paid quant positions likely would be primarily quant developers, which is more of a software development position where the individual is not required to have as much math and financial expertise. An excellent quant developer could certainly earn $250,000, but that's about as high as the compensation package generally would go.

Despite the high pay level, some quants do complain that they are "second-class citizens" on Wall Street and don't earn the multimillion-dollar salaries that top hedge fund managers or investment bankers command. As you can see, financial success is always relative.

The estimated median total pay of a quantitative analyst in the U.S. in 2024. Google is among the highest-paying companies for a quant, offering a median annual pay of $262,000.

Quants Skills and Education

Financial knowledge.

Many financial securities, such as options and convertibles , are easy to understand conceptually but are very difficult to model precisely. Because of this hidden complexity, the skills most valued in a quant are those related to mathematics and computation rather than finance.

It is a quant's ability to structure a complex problem that makes them valuable, not their specific knowledge of a company or market.

A quant should understand the following mathematical concepts:

• Calculus (including differential, integral, and stochastic)
• Linear algebra and differential equations
• Probability and statistics

Key financial topics include:

• Portfolio theory
• Equity and interest rate derivatives, including exotics
• Credit-risk products

Some quants will specialize in specific products, such as commodities, foreign exchange (Forex), or asset-backed securities .

Computer Competency

Software skills are also critical to job performance. C++ is typically used for high-frequency trading applications, and offline statistical analysis would be performed in MATLAB, SAS, S-PLUS, or a similar package.

Pricing knowledge may also be embedded in trading tools created with Java, .NET, or VBA , and are often integrated with Excel. Monte Carlo techniques are essential. A majority of the work is also realized in Python, as scripting-type languages are good for running lots of data and multiple scenarios.

Education and Certifications

Most firms look for at least a master's degree or preferably a Ph.D. in a quantitative subject, such as mathematics, economics, finance, or statistics. Master's degrees in financial engineering or computational finance are also effective entry points for quant careers.

Generally, an MBA is not enough by itself to obtain a quant position, unless the applicant also has a very strong mathematical or computational skill set in addition to some solid experience in the real world. 

While most financial certifications, such as the Chartered Financial Analyst (CFA) designation likely wouldn't add much value to a prospective quant's resume, one that may is the Certificate in Quantitative Finance (CQF) —which you may earn globally via distance learning in a six-month intensive program.

Clearly, you need to have "the right stuff" to be a quantitative analyst. It requires both the intellectual ability to master complex and abstract mathematical domains and a willingness to tackle challenges that can seem insurmountable—all while under considerable pressure—which only a select few can do.

But that also doesn't mean that everyone who has the ability to be a quant should become one. The financial problems that quants face are very abstract and narrow. Unlike fundamental or qualitative analysts , quants don't read annual reports, meet with management, visit operations, prepare roadshows, or talk to shareholders. Most of their time is spent working with computer code and numbers on a screen.

Individuals with strong analytical skills are valuable in many different areas of finance, such as economic and financial analysis, for example. Having to compete against the best and brightest quants every single day may not be the quickest path through the ranks, especially for those with broader skills and interests and a desire to manage.

Another career issue to consider is that many Ph.D. quants who come from academic environments find they miss the research environment. Instead of being able to study a problem for several months, when supporting a trading desk you need to find solutions in days or hours. This usually precludes making any breakthroughs in the field. 

Do Quants Get Paid Well?

Yes, quants tend to command high salaries, in part because they are in demand. Hedge funds and other trading firms generally offer the highest compensation. Entry-level positions may earn only $120,000 to $210,000, but there is usually room for future growth in both responsibilities and salary and the ability to earn upwards of $300,000.

How Hard Is Quant Finance?

It takes advanced-level skills in finance, math, and computer programming to get into quantitative trading, and the competition for a first job can be fierce. Once someone has landed a job, it then requires long working hours, innovation, and comfort with risk to succeed.

Do You Need a Ph.D. to Be a Quant?

Having a Ph.D. in a subject like math, finance, economics, or statistics can be a definite plus for anyone wanting to become a quant. However, a master's degree in computational finance or financial engineering can also be the ticket to a career as a quantitative analyst.

Success in quantitative analysis is largely based on knowledge, talent, merit, and dedication instead of the ability to sell, network, or play politics. The quants who work in the field are there because they can do the job well—an environment that many find remarkably refreshing—and they are justly rewarded for their work.

Bureau of Labor Statistics. " Financial Analysts ."

Glassdoor. " Quantitative Analyst Salaries ."

Bureau of Labor Statistics. " Financial Analysts: Pay ."

Glassdoor. " How Much Does a Junior Quantitative Analyst Make? "

Glassdoor. " How Much Does a Quantitative Analyst Make? "

Bureau of Labor Statistics. " Financial Analysts: How to Become One ."

Certificate in Quantitative Finance. " Who Is It For? "

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Quantitative researchers build the models that power our quantitative strategies and inform many of our investment decisions.

Envision a possibility. Assemble the data. Test hypotheses. Build advanced statistical models that help us capture market opportunities.

Where Autonomy Meets Teamwork

Sharpen your specialized skills by conducting research that will quickly be applied, refined and tested. Our work goes into production with extraordinary speed, enabling you to make an impact quickly.

Quantitative Research at Citadel

Accelerate Your Early Career

Finance fundamentals.

Become fluent in the language of financial technology and adept at working with financial data. Learn the fundamentals of capital markets, investments and derivatives.

Applied Learning

Sharpen your exceptional research skills and hone them on real world problems. Benefit from mentorship from experts in quantitative finance.

Continuous Education

Develop new skills through a huge range of courses and certifications. Take advantage of over 50 annual instructor-led courses for employees.

Leadership Training

Prepare for career advancement with practical instruction on running teams, managing projects and communicating with colleagues.

Key Information for Applicants

Quantitative researchers from our team answer some of our candidates’ frequently asked questions.

What makes someone successful as a quantitative researcher?

Applying a curious and commercial mindset is crucial to success. Whether you are analyzing asset behavior under different circumstances, identifying risk factors or deciphering patterns from vast datasets, it’s essential to demonstrate these qualities:

We encourage initiative. Those who take on responsibility and accountability for projects are supported and rewarded and grow the fastest.

The problems we take on generally don’t have simple solutions. You will be exposed to real-world problems and must innovate and iterate from multiple angles with passion and persistence. We are excited by the opportunity to test our ideas—and keep doing so until we find a solution.

Ambition and Resilience

You should have an insatiable drive to seek out problems and identify their solutions. There is no shortage of interesting issues to work on, and the most successful quants are true self-starters who are eager to take on new challenges and solve difficult challenges.

Our work is challenging. People who thrive here embrace failure as an opportunity to learn and keep pushing forward. You need to be able to share strong rationales for the solutions you propose and must be comfortable pivoting.

Technical proficiency

Excellence is our baseline. Expertise in the financial markets is not necessarily required on Day One; we’ll teach you. We want talented researchers who place a high degree of importance on precision, want to learn, can master new skills and continue to raise the bar together.

What skills are the most important in quantitative research careers?

Math and statistics.

It’s vital to have these foundational skills, since you’ll be spending a lot of time modeling and forecasting, using statistics as a launching point. We can teach you everything you need to know about finance, but you should already have a robust statistical intuition.

Engineering and Coding

Coding can be very useful in quantitative roles. Familiarity with computer science will help you learn quickly and use our proprietary tools independently.

Working with Data

Prior experience working with data will enhance your ability to understand its real-world applications and model that information effectively. Many of your colleagues will come from non-computer science backgrounds like chemistry or physics, where they utilized these skills. This is invaluable as we develop new approaches to identify market signals.

Applied Research

A deep understanding of conducting and applying research is critical. Markets change daily and positions may need to be kept open for an extended period. To that end, experience working on long-term research projects is extremely beneficial, but learning on the job is equally effective.

What is the difference in the roles quantitative researchers play in fundamental and quantitative strategies?

Our asset management business includes: fundamental and quantitative strategies. Both approaches require top research talent – the main difference lies in how the models developed by our researchers are used to make investment decisions.

In our fundamental businesses, quants develop models that are used by our investment and trading professional to make decisions.

In Global Quantitative Strategies, quants work closely with engineers to build models that trade systematically.

What sets Citadel apart in the financial industry?

We only hire extraordinary talent. Our colleagues are extremely smart, driven individuals focused on achieving results. The opportunity for learning and growth is unrivaled.

Success is shared. We work alongside our peers and mentors to develop ideas together. When quants present their work to their research team, they receive valuable academic-style feedback. Teams pressure-test ideas from all angles, asking nuanced questions and challenging specific elements to ensure the concept is as strong as possible.

We can also make an impact faster here than just about anywhere else. Some firms keep research and production separate. Here, we combine the two, making us faster and more agile. A new project might go from ideation, to deployment, to the investment and trading teams within an hour.

What does a career path in quantitative research look like?

When you start, you will join a specialized team and have the opportunity to quickly learn and master new skills. Your success depends on your ability to apply yourself in a collaborative environment, one in which you can turn your ideas into measurable business impact.

Citadel provides the resources and mentors you need to be successful. There are countless opportunities to raise your hand with ideas and receive the tools to translate those lightbulb moments into reality. Flawless execution of these proposals will only lead to more opportunities to explore your solutions and demonstrate their value.

As you progress, you can take control of core strategies, new ideas and features. There is a real opportunity to own the entire pipeline—from generating an idea, to determining its viability, to implementing code, and finally, to deploying the solution for the business.

A true meritocracy, we foster an environment where people who demonstrate initiative and capability keep both their projects and careers accelerating.

What can you expect to learn as a quantitative researcher?

You will have the opportunity to hone your research expertise, improve your technical skills and develop your intuition.

When you work in the financial markets, you won’t wait weeks or months to see if you were right or if your hypothesis held. Projects are put into production and live trading. The markets give you real-time feedback, which helps you iterate and learn faster.

Since attention to detail is key, our quants continually sharpen their coding skills and become more confident in their ability to consistently deliver stable and production-ready code.

Our quants also develop a strong intuition for identifying what is important when they are testing and researching projects. Sometimes a small anomaly can lead to a large discovery, and they learn to embrace that way of thinking as they develop their intuition.

What is the firm’s culture like for quantitative researchers?

Collaboration is at the heart of our culture. We learn not only from our research, but from one another. Everyone focuses on new ways to contribute and add value.

We encourage the dynamic, experimental and autonomous approach familiar in many leading tech startups but provide the supportive framework of an established financial firm. This means you can benefit from mobility, communication, resources and top-tier benefits.

Our researchers come from a variety of backgrounds and thrive because they bring their unique perspectives and research specialties to bear. While each quant is expected to be a subject matter expert in a particular vertical, sharing and challenging ideas is necessary develop and deliver optimal solutions. Everyone continually thinks about how they can add value and what they can do to contribute.

Finally, we are empowered to contribute in meaningful ways. We’re motivated to do more than we ever thought possible – and are recognized for our accomplishments.

What Defines Strong Candidates

• BS/MS/PhD in mathematics, statistics, physics, computer science or another highly quantitative field
• Strong knowledge of probability and statistics
• Experience performing data analysis and building predictive models using data
• Intellectual curiosity
• Passion for problem solving
• Programming experience
• Motivation to learn

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Benefit from practical instruction on leading teams, managing projects and communicating with colleagues.

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A Day in the Life of a Quantitative Researcher

Answers for applicants.

Quants from our team answer some of our candidates’ most frequently asked questions.

What makes someone successful as a quant?

We encourage initiative. People who take responsibility and accountability for projects are supported and rewarded and grow the fastest.

Attention to detail

Excellence is our baseline. But you don’t need to be an expert on Day One; we’ll teach you. We want talented researchers who place a high degree of importance on precision, want to learn, can master new skills and continue to raise the bar together.

The problems we tackle do not have simple, check-the-box solutions. You will be exposed to real-world problems and must possess the ability to innovate and iterate from multiple angles with passion and persistence. We are excited by the opportunity to test our ideas—and keep doing so until we find a solution.

Ambition and resilience

You should have an insatiable drive to seek out problems and identify their solutions. There is no shortage of interesting problems to work on, and the most successful quants are true self-starters who are eager to take on new challenges and solve difficult problems.

Our work is challenging. People who thrive here embrace failure as an opportunity to learn and keep pushing forward. You need to be able to share strong rationales for the solutions you propose and must be comfortable pivoting.

What skills are the most important in quant careers?

We value candidates who have a deep understanding of technology and the research process. If those qualities describe you, we can help you define your area of expertise and locate your niche within the industry.

Math and statistics

It’s vital you possess these foundational skills, since you’ll be spending a lot of time modeling and forecasting, using statistics as a launching point. We can teach you everything you need to know about finance, but you should already have a robust statistical intuition.

Engineering and coding

Coding can be very useful in quantitative roles. Familiarity with computer science will help you learn quickly and get the most out of our proprietary tools.

Working with data

Prior experience working with data will enhance your ability to understand its real-world applications and model information effectively. Many of your colleagues will come from non-computer science backgrounds like chemistry or physics, where they utilized these skills. This is invaluable as we develop new approaches to identify market signals.

Applied research

A deep understanding of conducting and applying research is critical. Experience working on long-term research projects is extremely beneficial, but learning on the job is equally effective.

What sets our firm apart in the financial industry?

Some firms keep research and production separate. Here, we combine the two, making us faster and more agile. A new project can go from ideation, to deployment, to live trading within an hour.

We only hire extraordinary talent, without compromise. Our colleagues are extremely smart, driven individuals focused on achieving results. The opportunity for learning and growth at Citadel Securities is unrivaled.

Success is shared. We work with our peers and mentors to develop ideas together. When quants present work to their research team, they receive valuable academic-style feedback. The team pressure-tests ideas from all angles, asking nuanced questions and challenging specific elements to ensure the concept is as strong as possible. This dynamic engagement accelerates your growth and development.

What Makes a Great Candidate

• BS/MS/PhD in mathematics, statistics, physics, computer science or another highly quantitative field
• Strong knowledge of probability and statistics
• Experience performing data analysis and building predictive models using data
• Intellectual curiosity
• Passion for problem solving
• Programming experience
• Motivation and desire to learn
• Commitment to excellence

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Do you need a PhD for Quant?

Majority of the people in the quantitative finance industry participate in a masters or PhD program. If you go to a good university, can you get a Quant role from BA only?

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The Quantitative Researcher Career Path

What does a quantitative researcher do and how can you land a job as one..

A career in quantitative research can be a highly rewarding and lucrative path for those interested in the financial sector. These professionals, often called "buy-side quantitative analysts", are typically found in investment funds and proprietary trading firms. Their primary responsibilites consist of conducting advanced research and analysis to inform proprietary investment and trading strategies. In doing so, they often leverage statistical and machine learning techniques.

To begin your journey into the field of quantitative research, you should start by building a robust base foundation in mathematics and statistics. While a bachelor's degree at a top-ranked unviersity may be sufficient for some entry-level positions, a master's degree or even a PhD is preferable. Some firms won't even consider your application if it doesn't meet their educational background requirements. In addition to a strong academic background, previous experience in a quant-related field is also highly valued by employers. This can come in many forms, such as an internship in quant research, data science, or machine learning. If you're currently looking for quant internships check out OpenQuant .

In terms of compensation, you can expect your total compensation to fall in the range from $200,000 to $250,000 . Depending on the specific firm you work at, this number can be even higher. While the stability of the job can vary, it is generally considered to be medium, as the demand for these professionals is high, but the competition is extremely fierce. There aren't many jobs on the market and internships are scarce. This is why having some educational or professional credentials can really stand you apart as an applicant.

The work-life balance of a quantitative researcher can also be arduous, as the job can require long hours and a high level of focus and attention to detail. The level of stress can also be medium to high, as the financial markets are constantly changing, and researchers must be able to adapt and respond quickly to new developments.

Despite the demands of the job, a career as a quantitative researcher is considered to be very prestigious, as these professionals are responsible for conducting research and analysis that inform important investment and trading decisions. There are also decent career progression opportunities for QRs, as many pursue roles in portfolio management or other leadership positions within the financial industry after a few years.

Quantitative researchers also tend to work very closely with quant traders and quant developers. Oftentimes, quant researchers will help to develop models and algorithms that traders use to make decisions and may also help quant developers with analyzing the results of their simulations. In some cases, the responsibilities of these roles may be more specialized, depending on the financial market and/or fund type.

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Is Phd the only route to becoming a "Real" Quant?

• Thread starter Jarone Eugene McCorkle
• Start date 2/20/11

Jarone Eugene McCorkle

Nowadays, the more research I do on becoming a Quant, the more my hopes seem to dwindle off to nothingness. To be a Quant means to obtain a Phd, or some approximation thereof............... My hopes, to becoming a Quant is close to any others ambitions. I learned how to code for fun. I used to loath statistics & mathematics , but when applied to finance, I find myself in a room for hours measuring, collecting, interpreting, and forecasting financial outcomes. Attempting to understand Mr. Market , to measure risk and ultimate profitability has become an esoteric hobby of mine. And so, I am just like every other schmuck on this forum out there to fend for themselves & make a killing. Having said that, although I have entertained the idea of working with the big fishes, wide-eyed, molding financial instruments into shape on Wall Street for hours on end. Is there any hope for someone that wants to skip the Phd. route altogether, and just do the MFE, or MMF, and still have a chance at a front office gig after a couple years experience? Or are you doomed as "Jr. Quant," for life, and be pigeon holed into something far, far , far away from the money and action with no hopes unless you gain a Phd? I ask this becuase although I have done research online, I want to hear it from current students themselves or people working in industry. Of what prospects an MFE brings to those without PHd's. Are the options ( and pay :D) still good at least? Thanks in Advance.  

Andy Nguyen

@ Jarone Eugene McCorkle Let first establish some framework of agreement on your definition of "real quant". The landscape has changed, that much we need to agree on, so we are not arguing based on what you read from "My life as a quant" or glorified stories about quant career that were published 5 years ago, at the rise of the quant.  

...is there any hope for someone that wants to skip the Phd. route altogether, and just do the MFE, or MMF, and still have a chance at a front office gig after a couple years experience? Click to expand...

Andy Nguyen said: @ Jarone Eugene McCorkle Let first establish some framework of agreement on your definition of "real quant". The landscape has changed, that much we need to agree on, so we are not arguing based on what you read from "My life as a quant" or glorified stories about quant career that were published 5 years ago, at the rise of the quant. Click to expand...

Tsotne said: Let's take a look on many CEOs, CFOs on Wall Street. Do they all hold PhDs. NO. They don't. many of them are not doctors but are suited for financial world with the degrees they have. For example, as I remember the J.P. Morgan & Chase CEO, Jamie Dimon holds MBA from Harvard. Nor it is a MUST to hold the PhD degree from the field you are working at. Another example: Goldman Sachs CEO, Lloyd Blankfein holds the JD degree from Harvard. Click to expand...

Obviously, there has been some pressure on the derivatives market, so I have no idea to what extent Quants deal with derivatives these days. But I am sure they are working on developing other instruments, or measuring some sort of risk on investment portfolios. Click to expand...

Jarone I don't know if you have read this http://www.quantnet.com/the-coming-glut-of-financial-engineers/ The problem with everything that you read about what quants do is that many of those roles no longer exists in today landscape. MFE programs traditionally train people for roles in derivatives pricing, structured products and there are little hiring in that area at the moment. Many of the roles you list are done by people whose title does not include "quant" in it. Title inflation is another thing you should be aware of. There are more roles that an MFE can easily qualify for these days. The old days when only a quantum physics PhD was needed to understand martingale models are over. Such once prized knowledge is now being taught in a FE bachelor degree. I'm going to repeat the old, tired mantra around here "you don't do a PhD for a career purpose". I was hired into a desk quant position in 2007 for a prop desk at an IB before I finished my MFE. These days, those positions are long gone as banks getting rid of their prop trading operation. Reading trade journals, you don't have the feeling that new products with 3 letters in them being invented by the quants any more. Instead, you hear more and more about risk, about high frequency trading. Risk management is an area that MFE graduates should explore as they are getting better compensated. So if your goal is to do blue sky research, you may want to stick with academia. Finance is mostly and always profit driven and demand is always in what appear trendy or political correct at the moment. Who knows what HFT will be 3 years from now with all the changing policy.  

I also forgot to point out a very popular interview of Wall Street hiring managers on how to get a quant job http://www.quantnet.com/quant-job-advice-from-wall-street-executives/  

Thanks alot for the feed back. Much appreciated, really. I have an off topic question. Do quants need series 7 or other licenses on top of their degrees?  

No. Series 7 is for another line of work http://en.wikipedia.org/wiki/General_Securities_Representative_Exam  

Alright just making sure. Because I have seen jobs such as "Quant Trader, " or etc etc, so I naturally assumed series 7. Not sure if quant trading is related to FE .  

I had to pass Series 7 few years ago when I was in IB (corporate finance/Advisory). Not that I needed it at all, but it was a reg requirement. Same applies for Series 63.  

Hi, this is my first post and it seems this thread applies to me as well. I'm nearly finished my undergraduate degree and feeling torn about whether to head into a MFE and head into the work world or to pursue a PhD. I am extremely interested in algorithmic trading for a variety of reasons. First of all, I have been practicing my analytical skills since I was inspired by a finance course and have done quite well. Using a long/short strategy, I was able to predict growth over ~700% with a subsequent fall bringing the 9 month total over 2100%. I must state that I was following a uranium company and the situation in Japan brought the short position even lower than it would have, otherwise 9 months would have yields around 1200%. Regardless, I feel the model I used could easily be reproduced algorithmically. Secondly, I have been following the development of HFT and feel the added liquidity which market makers are bringing is creating the opportunity to bring new financial services to the market. Essentially I want to become an algorithmic strategist and also work with a team to introduce these services to the world. It may sound bold, but this is a passion of mine. Furthermore, as this is my first post. I used to work in the oilfield in Alberta, so I have a good understanding of the oil/gas industry and mining in general. I think it gives me a lot of insight into the risks that involved in these industries. So, what I'm asking is for some suggestions on how I can best improve my career prospects. Would I be limiting my credibility if I don't pursue a PhD? My concern with taking this path is that timing is critical and I don't want to be caught in academia while all the excitement is taking place. In addition, I am in Germany and looking at graduate schools in Europe. I'm fairly new over here, so some insights into the environment here would be useful as well. Thank you.  

JasonHunt said: Hi, this is my first post and it seems this thread applies to me as well. I'm nearly finished my undergraduate degree and feeling torn about whether to head into a MFE and head into the work world or to pursue a PhD. I am extremely interested in algorithmic trading for a variety of reasons. First of all, I have been practicing my analytical skills since I was inspired by a finance course and have done quite well. Using a long/short strategy, I was able to predict growth over ~700% with a subsequent fall bringing the 9 month total over 2100%. I must state that I was following a uranium company and the situation in Japan brought the short position even lower than it would have, otherwise 9 months would have yields around 1200%. Regardless, I feel the model I used could easily be reproduced algorithmically. Secondly, I have been following the development of HFT and feel the added liquidity which market makers are bringing is creating the opportunity to bring new financial services to the market. Essentially I want to become an algorithmic strategist and also work with a team to introduce these services to the world. It may sound bold, but this is a passion of mine. Furthermore, as this is my first post. I used to work in the oilfield in Alberta, so I have a good understanding of the oil/gas industry and mining in general. I think it gives me a lot of insight into the risks that involved in these industries. So, what I'm asking is for some suggestions on how I can best improve my career prospects. Would I be limiting my credibility if I don't pursue a PhD? My concern with taking this path is that timing is critical and I don't want to be caught in academia while all the excitement is taking place. In addition, I am in Germany and looking at graduate schools in Europe. I'm fairly new over here, so some insights into the environment here would be useful as well. Thank you. Click to expand...

Joy Pathak said: For what company? Click to expand...

JasonHunt said: Patterson UTI, it's a drilling company. I wasn't involved in the financial side of the business, but I saw a lot of what happens on the ground and gained insights into the risks involved, how the weather affects productivity, how a changing economic climate affects the ability to acquire assets at a bargain price, etc. Click to expand...

Joy Pathak said: Yup. I can empathize. I worked for Syncrude R&D in Edmonton. Click to expand...

JasonHunt said: Wow, small world. I'm originally from Edmonton. How long have you been away? Did you leave because of work/school? Click to expand...

Joy Pathak said: I worked in 2008. I lived in Edmonton for a year. Mill"h00ds" represent! haha. Loved my time there. Went snowboarding almost every weekend to Banff or Jasper Click to expand...

JasonHunt said: Haha, I was pretty fond of biking in the rivervalley. I lived in Holyrood, so I was about 5 minutes from the valley on my bike. What are you doing now? Click to expand...

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Graduate Quantitative Researcher, PhD

As a Graduate Quantitative Researcher, you will develop, refine and implement algorithmic trading strategies that shape the future of electronic trading. Working alongside a research team of mathematicians, scientists and technologists, you will leverage vast data sets to construct complex models to predict market movements. With your expertise in statistics and exceptional analytical and research skills, you will develop innovative solutions that are foundational to Optiver’s trading strategies.

What you’ll do:

Your onboarding

You will participate in Optiver’s comprehensive Global Academy and be equipped with the knowledge needed to make an impact the moment you join your team. The comprehensive training covers trading theory and Optiver’s tech stack to hone your skills for your role. In addition, you will be paired with a dedicated mentor who will empower you to take ownership of your work and make a difference.

Your responsibilities

As a Quantitative Researcher, you will have the opportunity to contribute to several key areas:

• Using statistical models and machine learning to develop trading algorithms.
• Leveraging big data technologies to analyze high-frequency trading strategies, market microstructure and financial instruments to identify trading opportunities.
• Building stochastic models to determine the fair value of financial derivatives.
• Combining quantitative analysis and high-performance implementation to ensure the efficiency and accuracy of pricing engines and libraries.

What you’ll get:

You’ll join a culture of collaboration and excellence, where you’ll be surrounded by curious thinkers and creative problem solvers. Motivated by a passion for continuous improvement, you’ll thrive in a supportive, high-performing environment alongside talented colleagues, working collectively to tackle the toughest problems in the financial markets.

In addition, you’ll receive:

• A performance-based bonus structure unmatched anywhere in the industry. We combine our profits across desks, teams and offices into a global profit pool.
• The opportunity to work alongside best-in-class professionals from over 40 different countries.
• Ownership over initiatives that directly solve business problems.
• 401(k) match up to 50% and fully paid health insurance.
• 25 paid vacation days alongside market holidays.
• Extensive office perks, including breakfast, lunch and snacks, regular social events, clubs, sporting leagues and more.

Who you are:

• PhD in Mathematics, Statistics, Computer Science, Physics, or a related STEM field, with outstanding academic achievements
• Expected graduation date by mid-2025
• Availability to commence full-time employment upon graduation in 2025
• Solid foundation in mathematics, probability, and statistics
• Excellent research, analytical, and modeling skills
• Independent research experience
• Proficiency in any programming language
• Knowledge of machine learning, time-series analysis, pattern recognition, and NLP is a plus
• Strong interest in working in a fast-paced, collaborative environment
• Fluent in English with strong written and verbal communication skills

Diversity statement

Optiver is committed to diversity and inclusion .

For answers to some of our most frequently asked questions, refer to our Campus FAQs .

Optiver is supportive of US immigration sponsorship for this role.

*We accept one application per role per year. If you have previously applied to this position during this season and have been unsuccessful, you can reapply once the next recruitment season begins in 2025.

At Optiver, we are committed to creating a diverse and inclusive environment of mutual respect. Optiver recruits, employs, trains, compensates and promotes regardless of race, religion, color, sex, gender identity, sexual orientation, age, physical or mental disability, or other legally protected characteristics.

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Seven graduate students honored with Doctoral Dissertation Fellowships

MINNEAPOLIS / ST. PAUL (7/18/2024) – Seven graduate students advised by Department of Chemistry faculty members were recently awarded the University of Minnesota’s Doctoral Dissertation Fellowship. The seven students honored by this prestigious award are Kaylee Barr, Brylon Denman, Madeline Honig, Chris Seong, Sneha Venkatachalapathy, Murphi Williams, and Caini Zheng.

Kaylee Barr , a Chemical Engineering and Materials Science PhD student, is entering her fifth year in the Reineke Group . Before making the move to Minnesota, she received her BS in Chemical Engineering from the University of Kansas. “I came to the University of Minnesota because of the department's developments in polymer science, and because I was interested in the intersection of polymer science and drug delivery in Theresa Reineke's lab,” she says. Here at UMN, Kaylee studies how bottlebrush polymer architecture affects pH-responsive oral drug delivery. This summer, she is excited to grow professionally and as a scientist in an intern position at Genentech.

Brylon Denman is a Chemistry PhD candidate in the Roberts Group . She joined the UMN community in 2020 after completing her BS in Biochemistry at St. Louis University. “My research in the Roberts group seeks to resolve regioselectivity and reactivity issues within aryne methodology via ligand control,” Brylon says. “To accomplish this task, I have taken a mechanistic and hypothesis driven approach to understand how key molecular parameters modify regioselectivity and reactivity. I hope to use the knowledge I have gained from these studies to both improve the synthetic utility of aryne intermediates, and improve the sustainability of aryne reactions.” Brylon is also passionate about sustainable and green chemistry. As a founding member of the Sustainable and Green Chemistry committee, Brylon strives to collaborate with other department teammates to strengthen the culture of green and sustainable chemistry through integration into teaching, research, and community engagement. “In my career I aim to continue this advocacy and use my breadth of knowledge to enact sustainable change at a major pharmaceutical company as emphasizing sustainability on such a large scale can lead to a large impact,” she says. As she works through her internship at AbbVie this summer, Brylon is looking towards the future to outline her next steps after graduation.

Madeline Honig first experienced Chemistry at UMN during a summer REU experience in the Bühlmann Lab . She formally joined the Prof. Bühlmann's team in Fall 2020 after earning her BA in Chemistry from Earlham College. Her research here at UMN  has focused on the development and improved understanding of polymeric membrane-based ion-selective electrodes (ISEs). “One of my projects involves developing a quantitative parameter to better define the upper detection limits of these sensors which can be used to more accurately define sensor performance and predict the working range under different conditions,” Madeline says. “This research led us to investigate the unexplained 'super-Nernstian' responses of some pH-selective electrodes and expand the phase boundary model (the quantitative model that predicts ISE behavior) to include the formation of complexes between protonated ionophores and counter-ions in the sensing membrane. ISEs have been widely used for decades in clinical blood analysis among many other applications so it's exciting that I was still able to add to our fundamental understanding of how these sensors function.” One of Madeline’s goals is to use her research to enable the development of improved sensors that can be used in a wider range of conditions. Over the course of her graduate studies, Madeline has had the opportunity to be a graduate student mentor for two other students: Ariki Haba, a visiting master's student from Japan, and Katie O'Leary, a summer REU student, who both made significant contributions to the project. “Acting as a graduate mentor was really cool and I hope I can also make graduate-level chemistry research more approachable for everyone that I work with,” Madeline says. For her significant research efforts, Madeline was also recently selected in a national competition as one of the four winners of the 2024 Eastern Analytical Symposium Graduate Student Research Award. She will accept the award in November in Plainsboro NJ at the Eastern Analytical Symposium.

Chris Seong , an international student from New Zealand and PhD candidate in the Roberts Group, came to UMN after completing his BA with Distinction in Chemistry at St. Olaf College in 2020. Chris’ overarching chemistry interests involve the development of methods to utilize naturally abundant carboxylic acids as feedstock to synthesize medicinally relevant products, which are traditionally made with non-renewable starting materials derived from fossil fuels. “My earlier work has been focused on making alkyl-alkyl bonds through decarboxylation, but lately, in true Roberts Group fashion, I have turned my attention to using a similar mechanism to do aryne chemistry,” Chris says. He is currently working to publish a paper on the aryne project that he has been working on with two talented group mates; Sal Kargbo and Felicia Yu. “I am really excited to share this cool chemistry with the world,” he says. Outside of the lab, Chris is working on expanding his network to apply for jobs in the pharmaceutical industry – specifically in the early process space.

Sneha Venkatachalapathy is a member of the Distefano Group and an international student from India. She completed her BS in Chemistry with a minor degree in Biotechnology from Shiv Nadar University, Greater Noida, India in 2020. “Chemistry has always been my passion since high school. I still remember my first successful brown ring test that has left a remarkable fascination and interest towards chemistry,” Sneha says. “This early fascination has driven my academic journey, guided by mentors like Dr. Subhabrata Sen, who encouraged me to pursue a PhD in the United States.” Sneha was drawn towards working in the Chemical Biology research field where she could directly contribute to developing human life. “Joining Dr. Mark Distefano’s lab at UMN provided me with the chance to collaborate with Dr. Mohammad Rashidian from Dana Farber Cancer Institute. Together, we work towards expanding the scope of protein prenylation to construct protein-based cancer diagnostic tools,” she says. Sneha’s goal for her time in the UMN PhD program is to create innovative protein-based tools for cancer detection and treatment, aiming to enhance patient’s quality of life. She says she is looking forward to continuing to develop her leadership skills as she continues her doctorate, and is also exploring future opportunities beyond UMN. “One thing that motivates me daily is the belief that my research contributions to the scientific community would enhance our understanding of cancer diagnostic methods, ultimately leading to improved patient outcomes worldwide,” she says.

Murphi Williams  completed her undergraduate studies at the University of Wisconsin-Eau Claire, then joined the Bhagi-Damodaran at UMN in 2020. When it comes to research, Murphi is interested in chemical biology, more specifically, looking into proteins involved in important biological problems. “One of my major projects is developing and characterizing a potential inhibitor for  Mycobacterium tuberculosis , the bacteria that causes tuberculosis,” Murphi says. “Tuberculosis is the leading infectious disease so my projects center on understanding and inhibiting heme proteins important for the bacteria. Specifically, a previous lab member identified a small molecule that I've been characterizing the activity of in cells.” Her current research goal is to express and purify the protein targets for her small molecule inhibitor in the lab to further demonstrate the in vitro activity. She is also contemplating a future career in science communication. Outside of the lab, she enjoys working on her garden. 

Caini Zheng joined the Chemistry at the UMN in 2019 after finishing her undergraduate studies at Shanghai Jiao Tong University. She is currently a sixth-year graduate student co-advised by Profs. Tim Lodge and Ilja Siepmann . Her research focuses on the phase behavior of soft materials, including polymers and oligomers. Her DDF statement is titled "Self-Assembly of Polymers and Amphiphiles into Bicontinuous Phases". Caini is currently working on a project to elucidate the self-assembly of glycolipids through molecular dynamics simulations coupled with machine learning methods. In the future, she wants to work in the industry on bridging data science with traditional material research.

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Meet EPA Researcher Amalia Handler, Ph.D.

EPA researcher Amalia Handler works on modeling nutrients and harmful algal blooms in waterbodies across the lower 48 states of the US. Currently her main project is modeling the spatial drivers of harmful algal blooms in lakes that are caused by cyanobacteria across the lower 48 states. The project is investigating the landscape, climate, and lake characteristics that are associated with blooms by leveraging multiple EPA datasets including the National Lakes Assessment , the National Nutrient Inventory , and LakeCat . The aim is to estimate cyanobacteria and risk of a common cyanobacterial toxin, microcystin, in as many lakes as possible across the US. It’s a follow-up on previous research on the estimation of the risk of microcystin among satellite-monitored lakes .

Tell us about your background.

I have a bachelor’s degree in environmental science from Franklin & Marshall College. I earned my Ph.D. at Arizona State University in the lab of Nancy Grimm, Ph.D. I studied nitrogen cycling in streams and wetlands using a combination of biogeochemical and ecosystem ecology approaches that included field and lab experiments as well as network and statistical modeling.

When did you first know you wanted to work in environmental science? 

I had a mentor early in my career that shared with me the remarkable microscopic world of soils—a dynamic mixture of microbes, plants, fungi, minerals, detritus, and so much more. He would say, “The biggest things in this world are done by the smallest organisms.” That feeling of being in awe of these organisms stuck with me. It continues to inspire my work on how small organisms can control big things, like nitrogen cycling, water quality, even the climate.

What do you like most about your job? 

Two things, really. First, I get to work with an amazing group of researchers who are eager to collaborate and provide great insights. Second, almost every person has had some personal experience with harmful algal blooms, so it’s very meaningful work.

How does your science matter? 

Unfortunately, harmful algal blooms are widespread, but getting data on the enormous number of waterbodies across the country is really challenging. The research I do leverages the data where we have it and estimates the risk of blooms at unmeasured lakes. This estimation helps identify the number and location of lakes that may need further bloom monitoring.

If you weren’t a scientist, what would you be doing? 

I think I would be a professional facilitator. I really enjoy bringing people together for discussions and leading project teams. I learn a tremendous amount from others in these settings. 

What advice would you give a student interested in a career in science?

Try out many different fields of science. I have had experiences in everything from conducting conservation activities in state parks, to public health as it related to lead exposure in children, to researching the extent to which foxes rely on human food . There are so many fields of science and ways to be a scientist!

What do you think the coolest scientific discovery was and why? 

The endosymbiotic theory. This is the concept that eukaryotic cells (those in plants and animals) developed from larger cells by engulfing smaller, free-living cyanobacteria. The cyanobacteria eventually continued to function inside the larger cell and, voilà, there are now cells with organelles like mitochondria and chloroplasts! I think it’s so neat that the cells that make up the human body are the product of a cooperative evolutionary process.

If you could have dinner with any scientist, past or present, who would you choose and what would you talk about? 

I am a huge fan of the writings of Oliver Sacks, and it would be a pleasure to have dinner with him. Sacks was a physician and wrote fascinating and deeply caring accounts of patients he saw with neurological disorders. His books made me think about science as a field that inspires both awe and compassion.

Editor's Note:  The opinions expressed herein are those of the researcher alone. EPA does not endorse the opinions or positions expressed.  

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Susan Nochajski PhD Grant Research Fund

Support social work research through this fund, named for a beloved former faculty member.

The PhD Grant Research Fund was created to celebrate the memory of UB faculty member Susan M. Nochajski, who passed away in 2015. The fund provides support for PhD students for research and scholarly initiatives in the School of Social Work.

An occupational therapist with more than 20 years of clinical experience working primarily with persons with developmental disabilities, Nochajski was a great teacher, excellent mentor and compassionate friend.

A prolific scholar, she was an investigator on more than 10 grants totaling more than $5.5 million and published extensively in her field. She was on the editorial board of the American Journal of Occupational Therapy and Occupational Therapy in Health Care, and was a grant reviewer for the U.S. Department of Education.

Nochajski was a fellow of the American Occupational Therapy Association and a member of the New York State Occupational Therapy Association.

She earned a BS and MS in occupational therapy and a PhD in special education, all from UB.

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Research Assistantship (2109-2024)

Department: The University Museum (75750)

Position: Research Assistantship

Hours/Week: 25% (10 hours)

Term: Fall 2024

Closing Date: 9/10/2024

Location: SIUE

Number of Openings: 1

Semi-monthly Stipend: $300.00

Posting Number: 2109-2024

Qualifications

Applicants must currently be working towards masters degree in one of the following disciplines: History, Museum Studies, Cultural Heritage & Resource Management, Public Administration & Policy Analysis, or Art & Design. Applicants must be enrolled in a minimum of 6, but no more than 12 credit hours per academic term for fall and spring. Applicants must maintain at least a 3.0 grade point average.

Position Description

Assist with the inventory, cataloging, evaluation, research, display, and maintenance of museum objects, records, and facility. (Cost share for grant #MA-253223-OMS-23, BP#721961) 

Application Process

Required documents.

Employment Application Form, Cover letter, Resume, one letter of recommendation. 

Applicant Materials Are Received By

 Email In Person

  **All communications sent to the prospective employer should include the posting number for the position. 

Voluntary GA Applicant Data Form

This form is used to gather applicant data by the Office of Equal Opportunity, Access, & Title IX Coordination at SIUE. This information cannot be viewed by units hiring Graduate Assistants. Completion of this form is not required and will not impact your chances of being hired for a Graduate Assistantship. This form is NOT an application for employment. To apply for a Graduate Assistant position, follow the instructions listed in the posting.  

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