physics phd quant

Princeton Quantum Initiative

Quantum Science and Engineering PhD Program

PQI is launching a new PhD program in Quantum Science and Engineering, accepting applications in Fall 2023.

Find full information about the program structure and requirements  from Princeton Graduate School. The application for the program can be found through the Graduate School portal .

The PhD program in Quantum Science and Engineering provides graduate training in a new discipline at the intersection of quantum physics and information theory. Just as the 20th century witnessed a technological and scientific revolution ushered in by our newfound understanding of quantum mechanics, the 21st century now offers the promise of a new class of technologies and lines of scientific inquiry that take full advantage of the more fragile and intricate consequences of quantum mechanics: coherent superposition, projective measurement, and entanglement. This field has broad implications ranging from many-body physics and the creation of new forms of matter to our understanding of the emergence of the classical world and our basic understanding of space and time.  It enables fundamentally new technological applications, including new types of computers that can solve currently intractable problems, communication channels whose security is guaranteed by the laws of physics, and sensors that offer unprecedented sensitivity and spatial resolution.

The Princeton Quantum Science and Engineering community is unique in its interdisciplinary breadth combined with foundational research in quantum information and quantum matter. Research at Princeton comprises every layer of the quantum technology stack, bringing together many body physics, materials, devices, new quantum hardware platforms, quantum information theory, metrology, algorithms, complexity theory, and computer architecture. This vibrant environment allows for rapid progress at the frontiers of quantum science and technology, with cross pollination among quantum platforms and approaches. The research community strongly values interdisciplinarity, collaboration, depth, and fostering a close-knit community that enables fundamental and impactful advances.

Our curriculum places students in an excellent position to build new quantum systems, discover new technological innovations, become leaders in the emergent quantum industry, and make deep, lasting contributions to quantum information science. The QSE graduate program aims to provide a strong foundation of fundamentals through a three-course core, as well as opportunities to explore the frontiers of current research through electives. First year students are also required to take a seminar course that is associated with the Princeton Quantum Colloquium, in which they closely read the associated literature and discuss the papers. Our curriculum has a unique emphasis on learning how to read and understand current literature over a large range of topics. The curriculum is complemented by many opportunities at PQI for scientific interaction and professional development. A major goal of the program is to help form a tight-knit graduate student cohort that spans disciplines and research topics, united by a common language. 

Most students enter the program with an undergraduate degree in physics, electrical engineering, computer science, chemistry, materials science, or a related discipline. When you apply, you should indicate what broad research areas you are interested in: Quantum Systems Experiment, Quantum Systems Theory, Quantum Materials Science, or Quantum Computer Science.

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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Quantum Science and Engineering

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In this first-of-its-kind quantum science and engineering program, you will be a part of an interdisciplinary program that builds on Harvard’s track record of excellence in the field. The flexible curriculum will equip you with a common language for the rapidly growing field of quantum science and engineering (QSE). You will have the opportunity to work with faculty from both the science and engineering programs to design an individualized path tailored to your QSE research interests. Research is a primary focus of the program, and you will be working with state-of-the-art experimental and computational facilities.

You will be embedded in the broader Boston-area quantum ecosystem through collaborations with institutions such as MIT and University of Massachusetts Boston. You will also have the opportunity to interact with industry partners working to turn quantum theory into useful systems and devices.

Graduates of the program are uniquely qualified to meet the rising demand for QSE researchers and educators in industry, academia, and national labs.

Additional information on the graduate program is available from the PhD program in quantum science and engineering and requirements for the degree are detailed in Policies .

Areas of Study

Applied Physics | Computer Science | Electrical Engineering | Experimental Physics | Physical Chemistry | Materials Science and Engineering | Theoretical Chemistry | Theoretical Physics | Quantum Devices, Quantum Materials | Unspecified

Admissions Requirements

Please review the admissions requirements and other information before applying. You can find degree program-specific admissions requirements below and access additional guidance on applying from the PhD program in quantum science and engineering.

Academic Background

Students with bachelor’s degrees in physics, mathematics, chemistry, computer science, engineering, or related fields are invited to apply for admission. Prospective students should demonstrate depth of background in one or more relevant fields including (but not limited to) physics, electrical engineering, engineering sciences, materials science, computer science, and chemistry. Typically, applicants will have devoted approximately half of their undergraduate work to one or more of these fields and will have demonstrated overall academic excellence.

Statement of Purpose

Applicants should detail their reason for pursuing the PhD in quantum science and engineering and explain why this program is particularly well-suited for them. A student who has a marked interest in a particular area of quantum science and engineering should include this information in the online application. If possible, applicants should also indicate whether they are inclined toward experimental or theoretical (mathematical) research. This statement of preference will not be treated as a binding commitment to any course of study and research.

Personal Statement

Not Accepted

Standardized Tests

GRE General: Optional GRE Subject Test: Optional

See list of Quantum Science and Engineering faculty

APPLICATION DEADLINE

Questions about the program.

Quantum at the University of Chicago

Quantum science and engineering.

The University of Chicago has assembled a world-class group of scientists and engineers who are leading the quantum revolution. In addition to our cutting-edge faculty in physics, engineering, and chemistry, we offer one of the nation's first doctoral programs in quantum science and engineering.

• 20+ lab groups at PME and UChicago engaged in quantum research
• Chicago region named an official U.S. Regional and Innovation Technology Hub for quantum technologies
• Close partnerships with National QIS Research Centers at Argonne and Fermilab

Learn about our many exciting programs and centers

Noah Glachman

Bernien Lab

“Quantum computing has the potential to solve some of the world's biggest problems. I'm proud to be a part of a team here making that happen.”

Anchita Addhya

“PME brings these diverse fields together and has this very collaborative environment that I really appreciate.”

José A. Méndez

Awschalom Lab (co-advised by Hannes Bernien)

“Study something that you find interesting and I guarantee we can use you here.”

We are training students to become the next generation of leaders in science, engineering, and beyond. ” Dean Nadya Mason

Why Engineering at the University of Chicago?

• 100% of PhD students start research in their first year
• PME’s leading faculty experts unite eight scientific and engineering disciplines through an innovative interdisciplinary curriculum
• Students work with 40+ internationally recognized faculty members and 20+ researchers from Argonne National Laboratory
• More than 200 PME graduates have entered roles as data scientists, engineers, and research consultants, or continued their research at academic institutions.

Engineer Your Future. Start Here.

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Many PhD students in the MIT Physics Department incorporate probability, statistics, computation, and data analysis into their research. These techniques are becoming increasingly important for both experimental and theoretical Physics research, with ever-growing datasets, more sophisticated physics simulations, and the development of cutting-edge machine learning tools. The Interdisciplinary Doctoral Program in Statistics (IDPS)  is designed to provide students with the highest level of competency in 21st century statistics, enabling doctoral students across MIT to better integrate computation and data analysis into their PhD thesis research.

Admission to this program is restricted to students currently enrolled in the Physics doctoral program or another participating MIT doctoral program. In addition to satisfying all of the requirements of the Physics PhD, students take one subject each in probability, statistics, computation and statistics, and data analysis, as well as the Doctoral Seminar in Statistics, and they write a dissertation in Physics utilizing statistical methods. Graduates of the program will receive their doctoral degree in the field of “Physics, Statistics, and Data Science.”

Doctoral students in Physics may submit an Interdisciplinary PhD in Statistics Form between the end of their second semester and penultimate semester in their Physics program. The application must include an endorsement from the student’s advisor, an up-to-date CV, current transcript, and a 1-2 page statement of interest in Statistics and Data Science.

The statement of interest can be based on the student’s thesis proposal for the Physics Department, but it must demonstrate that statistical methods will be used in a substantial way in the proposed research. In their statement, applicants are encouraged to explain how specific statistical techniques would be applied in their research. Applicants should further highlight ways that their proposed research might advance the use of statistics and data science, both in their physics subfield and potentially in other disciplines. If the work is part of a larger collaborative effort, the applicant should focus on their personal contributions.

For access to the selection form or for further information, please contact the IDSS Academic Office at  [email protected] .

Required Courses

Courses in this list that satisfy the Physics PhD degree requirements can count for both programs. Other similar or more advanced courses can count towards the “Computation & Statistics” and “Data Analysis” requirements, with permission from the program co-chairs. The IDS.190 requirement may be satisfied instead by IDS.955 Practical Experience in Data, Systems, and Society, if that experience exposes the student to a diverse set of topics in statistics and data science. Making this substitution requires permission from the program co-chairs prior to doing the practical experience.

• IDS.190 – Doctoral Seminar in Statistics and Data Science ( may be substituted by IDS.955 Practical Experience in Data, Systems and Society )
• 6.7700[J] Fundamentals of Probability or
• 18.675 – Theory of Probability
• 18.655 – Mathematical Statistics or
• 18.6501 – Fundamentals of Statistics or
• IDS.160[J] – Mathematical Statistics: A Non-Asymptotic Approach
• 6.C01/6.C51 – Modeling with Machine Learning: From Algorithms to Applications or
• 6.7810 Algorithms for Inference or
• 6.8610 (6.864) Advanced Natural Language Processing or
• 6.7900 (6.867) Machine Learning or
• 6.8710 (6.874) Computational Systems Biology: Deep Learning in the Life Sciences or
• 9.520[J] – Statistical Learning Theory and Applications or
• 16.940 – Numerical Methods for Stochastic Modeling and Inference or
• 18.337 – Numerical Computing and Interactive Software
• 8.316 – Data Science in Physics or
• 6.8300 (6.869) Advances in Computer Vision or
• 8.334 – Statistical Mechanics II or
• 8.371[J] – Quantum Information Science or
• 8.591[J] – Systems Biology or
• 8.592[J] – Statistical Physics in Biology or
• 8.942 – Cosmology or
• 9.583 – Functional MRI: Data Acquisition and Analysis or
• 16.456[J] – Biomedical Signal and Image Processing or
• 18.367 – Waves and Imaging or
• IDS.131[J] – Statistics, Computation, and Applications

Grade Policy

C, D, F, and O grades are unacceptable. Students should not earn more B grades than A grades, reflected by a PhysSDS GPA of ≥ 4.5. Students may be required to retake subjects graded B or lower, although generally one B grade will be tolerated.

Unless approved by the PhysSDS co-chairs, a minimum grade of B+ is required in all 12 unit courses, except IDS.190 (3 units) which requires a P grade.

Though not required, it is strongly encouraged for a member of the MIT  Statistics and Data Science Center (SDSC)  to serve on a student’s doctoral committee. This could be an SDSC member from the Physics department or from another field relevant to the proposed thesis research.

Thesis Proposal

All students must submit a thesis proposal using the standard Physics format. Dissertation research must involve the utilization of statistical methods in a substantial way.

PhysSDS Committee

• Jesse Thaler (co-chair)
• Mike Williams (co-chair)
• Isaac Chuang
• Janet Conrad
• William Detmold
• Philip Harris
• Jacqueline Hewitt
• Kiyoshi Masui
• Leonid Mirny
• Christoph Paus
• Phiala Shanahan
• Marin Soljačić
• Washington Taylor
• Max Tegmark

Can I satisfy the requirements with courses taken at Harvard?

Harvard CompSci 181 will count as the equivalent of MIT’s 6.867.  For the status of other courses, please contact the program co-chairs.

Can a course count both for the Physics degree requirements and the PhysSDS requirements?

Yes, this is possible, as long as the courses are already on the approved list of requirements. E.g. 8.592 can count as a breadth requirement for a NUPAX student as well as a Data Analysis requirement for the PhysSDS degree.

If I have previous experience in Probability and/or Statistics, can I test out of these requirements?

These courses are required by all of the IDPS degrees. They are meant to ensure that all students obtaining an IDPS degree share the same solid grounding in these fundamentals, and to help build a community of IDPS students across the various disciplines. Only in exceptional cases might it be possible to substitute more advanced courses in these areas.

Can I substitute a similar or more advanced course for the PhysSDS requirements?

Yes, this is possible for the “computation and statistics” and “data analysis” requirements, with permission of program co-chairs. Substitutions for the “probability” and “statistics” requirements will only be granted in exceptional cases.

For Spring 2021, the following course has been approved as a substitution for the “computation and statistics” requirement:   18.408 (Theoretical Foundations for Deep Learning) .

The following course has been approved as a substitution for the “data analysis” requirement:   6.481 (Introduction to Statistical Data Analysis) .

Can I apply for the PhysSDS degree in my last semester at MIT?

No, you must apply no later than your penultimate semester.

What does it mean to use statistical methods in a “substantial way” in one’s thesis?

The ideal case is that one’s thesis advances statistics research independent of the Physics applications. Advancing the use of statistical methods in one’s subfield of Physics would also qualify. Applying well-established statistical methods in one’s thesis could qualify, if the application is central to the Physics result. In all cases, we expect the student to demonstrate mastery of statistics and data science.

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PhD track Quantum Science and Technologies (QUANTIX)

WHY ENROLL IN THIS PROGRAM?

Get ready for a PhD by starting research at an early stage

Be closely associated with the research activities carried out in a world-renowned innovation cluster

Benefit from individual and personalized supervision by a faculty member

• Description
• Associated Laboratories
• PhD Tracks Research Projects

Quantum Technologies have seen a dramatic development in the past few years. The realization of individual quantum systems and the control of new materials with unconventional properties has paved the way to the development of machines and protocols based on the most fundamental aspects of quantum mechanics, without classical counterparts, such as the superposition of states and entanglement. The demonstration of quantum supremacy in 2019 has been a major step, but many new challenges remain to be taken for the complete deployment of Quantum Technologies, at both the fundamental level and that of practical applications.

The Quantum Science and Technologies PhD track is organized around six pillars:

• Quantum Materials
• Quantum Simulation
• Quantum Computation
• Quantum Sensing and Metrology
• Quantum Communication and Networking
• Quantum Information Processing

It aims at providing the students with a high level education at the state of the art of quantum physics and quantum information processing in direct contact with forefront research in all these fields.

• Be trained to forefront challenges in quantum science and its technological applications
• Contribute to cutting-edge research in a word-leading research center
• Discover a multidisciplinary field at the frontier of theoretical and experimental physics, computer science, and applied mathematics.
• Discover the diversity of quantum technologies in the rich scientific environment of the Plateau de Saclay
• Become a leader of the next generation of reserachers and engineers in quantum science and technologies

Partner University

• Université Paris-Saclay

The five-year curriculum of the PhD track trains students in cutting-edge research for them to pursue international careers  in prestigious universities and academic labs or leading companies in quantum technologies. 

The PhD Track provides a five-year "à la carte" integrated Master and PhD program for particularly motivated and talented students aiming at preparing a career in academia or industry through an individualized research-oriented training program in Quantum Science and Technologies. Students will be attributed an academic tutor in their field of research from the very start of their studies at IP Paris. In coordination with their tutor, students will elaborate their own personal curriculum consisting of course work and research phases corresponding to their research interests and professional project.

During their first year, students will follow a selection of high level courses focused on quantum physics and its interfaces. It may include computer science and applied mathematics courses, as well as complementary modules allowing them to broaden their general scientific culture and to acquire complementary skills. At the same time, the students are immediately members of the research team of their tutor and participate in team activities and research discussions. This includes in particular attending relevant research seminars and potentially topical workshops. During the first year, students will work on a research project, in collaboration with their host team. A significant part of the second year will be devoted to a larger-scale research work, giving rise to a Master thesis and – most likely – first research publications. This is also the occasion to consolidate their choice for the topic of their PhD.

While it will still be possible to follow selected – more specialized – scientific courses and courses in secondary skills, the last three years of the PhD Track program will be mainly devoted to research work towards the PhD degree.

In addition to the weekly laboratory work, two mandatory full-time internships take place during the spring, one at the M1 level, the other at the M2 level. The duration and corresponding number of ECTS are at least those of the main Master in which the student is enrolled. The number of ECTS can be adapted depending on the duration of the internship.

Students have the opportunity to visit international partner universities.

All relevant laboratories of IP Paris and partner institutions, in particular

• Center for Theoretical Physics (CPHT, Ecole Polytechnique
• Laboratory for Applied Optics (LOA, Ecole Polytechnique/ENSTA)
• Laboratory for Condensed Matter Physics (PMC, Ecole Polytechnique)
• Laboratory for Information Processing and Communication Laboratory (LTCI, Telecom Paris)
• Laboratory for Irradiated Solids (LSI, Ecole Polytechnique)

Admission requirements

Academic prerequisites.

Completion with highest honors of a Bachelor in physics, including courses in quantum physics, at Institut Polytechnique de Paris or equivalent in France or abroad.

Evidence of research potential is essential as the main goal of such a PhD program is to train first class researchers. 

Students who have completed the first year of an equivalent program may exceptionally be directly admitted to the second year (4-year PhD program).

Language prerequisites

A certificate of proficiency in English (level B2) is required (TOEIC, IELTS, TOEFL, Cambridge ESOL), except for native speakers and students who previously studied in English.

How to apply

Applications are exclusively online. You will be required to provide the following documents:

• Transcript 
• Two academic references (added online directly by your referees)  
• CV/resume 
• Statement of purpose indicating which 2 choices of research subjects among the one listed on this page under the section "PhD Track Research Projects"

You will receive an answer in your candidate space within 2 months following the closing date of the application session. 

Fees and scholarships

Registration fees are available here

Find out more about scholarships

Please note that fees and scholarships may change for the following year.

Applications and admission dates

Coordinator.

• Luca Perfetti 

General enquiry

• [email protected]

When applying to the PhD Tracks in Physics, you should describe your preferred fields of study and research in your motivation letter. You are ecouraged to choose two preferred PhD Track subjects among the list below.   Since the posted offers do not cover the full spectrum of our activities , you can also visit the web pages of the 11 laboratories (CPHT, IPVF, LLR, LOA, LOB, LPICM, LPMC, LPP, LSI, LULI, Omega) affiliated to the physics department and indicate the research lines that interest you the most.

PhD Track research projects in “QUANTUM SCIENCE AND TECHNOLOGY”

• Correlated quantum matter and quantum information
• Ultrafast dynamics of electrons in quantum materials
• Re-using model results to determine materials properties: connector theory approach
• Collective electronic fluctuations and their influence on materials properties
• Spin-dependent charge dynamics in dilute nitride and defect-engineered semiconductor quantum structures and devices
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• Theory of Many-Body Quantum States
• Probing the quantum properties of spin defects in 2D materials
• Time-frequency quantum information processing
• Uncovering a new law of physics in quantum materials

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• Science Education and Careers

Can a Physics PhD Land a Quant Job in Finance?

• Thread starter googleveena
• Start date Jun 1, 2008
• Tags Job Physics Physics student Student
• Jun 1, 2008
• Physicists report new insights into exotic particles key to magnetism
• Microscopy breakthrough promises better imaging for sensitive materials
• Can quantum particles mimic gravitational waves?

A PF Asteroid

Your best bet would be to contact the people in the profession. Ask if they'd be willing to meet with you for lunch or let you job shadow them for a day. My guess (which isn't worth much) is that it is possible - although the more experience you have with financial models the better.  

A PF Planet

Take a look at this https://www.physicsforums.com/showthread.php?p=1586903#post1586903" on the same topic.  

A PF Molecule

I think, before the proliferation of professional masters programs in mathematical finance / financial engineering, it was fairly common to hire technical PhDs (in math, physics, engineering, etc.) with no background in finance as quants. Even with that said, it sounds like your background is a pretty good match (programming experience in C++, and it sounds like you have experience in numerical methods for solving PDEs), so with some self study of mathematical finance (from standard books like Hull) and of probability and random processes, I think you could present yourself as an extremely qualified candidate for some quant positions.  

• Jun 2, 2008

It's relatively normal for entry level quants to not have a formal financial background. Although you'll be expected to have a decent self-taught understanding. Assuming to fit all their other requirements (personality, etc) I see no reason you wouldn't be a suitable quant candidate. It'll still ultimately be down to perseverance and luck though, like any job. From what I've seen, they like to grab you before the end of the PhD though (it's easier to get a job offer whilst you're in the last year of your PhD than afterwards). Round these parts, I've seen them try to get people to give up the PhD and join them straight away, but I've never heard of anyone's quant-type job offer being withdrawn because they said they couldn't start for a few months. And you rise through the ranks quicker with one too. The key take home point: You need to apply for the jobs now.  

Ex1 said: It's relatively normal for entry level quants to not have a formal financial background.

• Jun 5, 2008

Hi George, Hoping you'll say hello (I sent you a private message). Dave Kosokowsky  

• Jun 6, 2008

Ask this question on the wilmott or quantnet forums  

Related to Can a Physics PhD Land a Quant Job in Finance?

1. what is a "quant job" and how does it relate to physics.

A "Quant job" refers to a job in the field of quantitative finance or quantitative analysis, which involves using mathematical and statistical models to analyze financial data and make investment decisions. Physics students are often sought after for these roles due to their strong analytical and mathematical skills, which are applicable to understanding and predicting complex financial systems.

2. What kind of skills and knowledge do I need to have as a physics student to get a "Quant job"?

To be successful in a "Quant job", physics students should have a strong foundation in mathematics, particularly in areas such as calculus, statistics, and linear algebra. They should also have a good understanding of financial concepts and markets, as well as programming skills in languages such as Python, R, and MATLAB.

3. Are there any specific courses or certifications I should take to prepare for a "Quant job"?

While there are no specific courses or certifications required for a "Quant job", it is recommended to take courses in finance, economics, and computer science to supplement your physics background. Additionally, there are many online courses and bootcamps available that specifically focus on quantitative finance and can help you gain the necessary skills and knowledge.

4. What types of companies or industries offer "Quant jobs" for physics students?

"Quant jobs" can be found in a variety of industries, including investment banks, hedge funds, asset management firms, and insurance companies. These roles may also be available in non-financial companies that utilize quantitative analysis, such as tech companies or energy companies.

5. What are the career growth opportunities for physics students in "Quant jobs"?

The career growth opportunities for physics students in "Quant jobs" are vast. With a strong foundation in mathematics and analytical thinking, physics students can advance to more senior roles such as quantitative analyst, portfolio manager, or risk manager. They may also have the opportunity to move into other areas of finance, such as data science or financial engineering.

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quant job for physics phd

• Thread starter googleveena
• Start date 6/15/08

googleveena

hi I am a physics grad student in UNC chapel hill. I am working on experimental side, but I use some computation for my data analysis. I have pretty good knowledge on c/ c++ , Matlab. I am seriously thinking of looking for Quant jobs after graduation. I have tarted reading the book Options, Futures, and other derivatives by John C. Hull. I am not planning to take any finance course. Is it possible to get a Quant job without any finance course/degree ? Any discussion is cool. Thank you Vee  

IlyaKEightSix

You want to work in computational finance without understanding finance? Hit the books a little bit more. If you can pull a physics phD and have C++ skills, you've more or less shown that you have the talent. And you can look around on LinkedIn: Relationships Matter I've seen a quant job that required a physics phD.  

I didn't mean I want to work without finance background. I am planning to build the background by reading books myself. I am worried a little bit because I won't have any finance degree. does that matter much? Thanks V  

Are you kidding me? Renaissance technologies will turn you *down* if you have a finance degree.  

EricFleming

IlyaKEightSix said: Are you kidding me? Renaissance technologies will turn you *down* if you have a finance degree. Click to expand...

phD in physics Hi Googleveena, I am in a similar situation you have. I believe that PhD in physics valuable than Master in FE.  

EricFleming said: Yes, and so would a number of other places. They don't want Finance degrees, they want Physics and Math ubernerds. Click to expand...

qualifications Here's my situation: I've just graduated with a Phd in physics from Princeton University and I'm interested in becoming a quant. My area was experimental particle physics which required data analysis with MATLAB. My c++ is rather weak as I haven't used it since my undergraduate days. Would I need to relearn c++ before my interviews? Also, I have no knowledge of finance. thanks, p  

Andy Nguyen

Nowadays, a Physics PhD no longer automatically qualifies you for a Wall Street job. The blue sky research work being done is far and few between if any. You need to get yourself something technical and practical aka coding skills, be it C++ , C#, SQL, VBA/Excel, anything that you can hit the ground running. As for finance part, I suggest you start with this list, starting from the top Master reading list for Quants, MFE - QuantNetwork - Financial Engineering Forum  

Before the crisis, you could have easily get a job. Nowadays, things are much more complicated. Use the reading list from Andy, it will really help you. In which field do you want to work as a quant ? (statistical arbitrage, pricing, ... )  

Should you know the field you want to work in as a quant if you are a PhD. How do you determine without working which field you like. I have a background in signal processing and computer vision. In computer vision I use PDE based methods and statistics for pattern recognition. What kind of job will be good for me then? Any suggestions?  

a lost of PhD in physics go in statistical arbitrage. Especially, your skills in signal processing could help you, I think.  

I recruit for quantitative hedge funds and have a fair amount of empirical data on background and skills one needs to be able to get that first interview. To answer the OP's question, no you absolutely don't need a background in finance to interview at a quantitative shop. Your Physics PhD would be looked upon very favorably. Note that a hard sciences PhD is highly regarded: Physics, Math, Stats, OR, EE, etc. We recently even had a Mechanical Engineering PhD secure a few interviews. The second thing that's really important is a background in C++ programming. It has almost become a baseline requirement. Most interviewers will quiz you on your knowledge of math and your knowledge of C++ . The third thing that matters, and most people don't think about this, is strong communication. And not just being able to speak well. But also the ability to listen well and respond accordingly. It's also very important that you be able to effectively convey what you know. In other words, it's not enough to have the knowledge, you need to be able to convey that to someone else as well. And finally, contrary to popular opinion, there are jobs for recent PhDs at quant shops right now. There's been more activity in terms of hiring in Q2 of this year than there was in the last three quarters or so.  

You come to the right place for some reading list http://www.quantnet.com/master-reading-list-for-quants/ http://www.quantnet.com/forum/threa...uants-mfe-financial-engineering-students.535/ The gist of a quant finance career is either you have ideas to make money or you have the technical skills to help others make money. And technical skills means programming one way or another. You can do anything from VBA/Matlab to C++ / python . Just go to www.quantfinancejobs.com take a look around and see how many jobs require programming. Read the guides and imagine where you fit in. See the interview books and see if you can answer them.  

How is the situation nowadays?  

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Wells Fargo

2025 quantitative analytics development program – capital markets (phd).

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About this role:

Wells Fargo is seeking talent to join the Quantitative Analytics Program. Learn more about the career areas and lines of business at wellsfargojobs.com  

Program Overview  

The Quantitative Analytics Program provides you with an overview of Wells Fargo and prepares you to develop, implement, calibrate, or validate quantitative models. It is designed to give you a blend of technical, business, and professional developmental opportunities that incorporates real-world experiences and prepares you for a career at Wells Fargo.  Upon completion of two six-month rotations, you will join a team within your program track. 

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

Placement group includes:   

Traded Products Model Development  

Market and Counterparty Risk Analytics  

Mortgage Model Development Center  

Model Risk Management  

Consumer Model Development Center  

In this role you will:  

Be part of a team that provides solutions to business needs and improves risk management processes  

Communicate solutions to stakeholders through presentations and written documentation  

Stay up to speed on industry challenges and new and innovative modeling techniques used across Wells Fargo to solve business problems or enhance business capabilities.  

  Ideal candidate for this role:  

Excellent computer programing skills and use of statistical software packages such as Python, R, SAS, SQL, Java, Spark, and C++  

Ability to communicate concepts and ideas, both verbally and written, and explain technical material to a non-technical audience.  

Energetic self-starter who proactively takes initiative, remains curious and has a genuine interest in learning and growth. 

Program Start Date:  July 2025   

Program Location:   Charlotte, NC  

Program Length: 24 months (Year 1: training and 2 rotations; Year 2: permanent placement)  

Required Qualifications:

2+ years of Quantitative Analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

Master’s degree or higher in statistics, mathematics, physics, engineering, computer science, economics, or quantitative discipline

Desired Qualifications:

​Currently pursuing PhD degree with an expected graduation date in December 2024-June 2025 OR graduated from a PhD program after May 1, 2022 and is currently completing a postdoc with emphasis in Mathematics, Operations Research, Physics, Engineering, Computer Science, Statistics, Quantitative Finance or related quantitative field.   

Perform mathematical model development and validation under the direction of experienced team members using Python, R, C++, SAS, SQL or other programming languages  

Experience and demonstrated knowledge in mathematical and numerical methods including Monte Carlo methods, differential equations, linear algebra, applied probability, and statistics   

Excellent verbal, written, and interpersonal communication skills 

Ability to work effectively, as well as independently, in a collaborative, change driven environment 

Intermediate Microsoft Office (Word, Excel, Outlook, and PowerPoint) skills 

Involvement in extracurricular enrichment activities through one or more of the following: volunteerism, student organization involvement, study abroad program(s), leadership position(s), non-profit involvement  

#earlycareers 

How to Apply: 

The 2025 Quantitative Analytics Development Program will be accepting applications through the Wells Fargo Career website.  

To search and apply for open positions in this program, please go to https://www.wellsfargo.com/careers/ and click on “Search Jobs”.  

Search by Job ID #: R-3865666 or by keywords, “2025 Quantitative Analytics Development Program – Capital Markets – PhD” and follow the instructions to complete your application. 

We Value Diversity  

At Wells Fargo, we believe in diversity and inclusion in the workplace; accordingly, we welcome applications for employment from all qualified candidates, regardless of race, color, gender, national origin, religion, age, sexual orientation, gender identity, gender expression, genetic information, individuals with disabilities, pregnancy, marital status, status as a protected veteran or any other status protected by applicable law. 

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Wells Fargo

2025 summer internship quantitative analytics program – early careers phd.

• Share This: Share 2025 Summer Internship Quantitative Analytics Program – Early Careers PhD on Facebook Share 2025 Summer Internship Quantitative Analytics Program – Early Careers PhD on LinkedIn Share 2025 Summer Internship Quantitative Analytics Program – Early Careers PhD on X

About this role:

Wells Fargo is seeking talent to join the Summer Internship Quantitative Analytics Program (PhD) – Early Careers.  Learn more about the career areas and lines of business at wellsfargojobs.com  

Program Overview 

The internship program is a ten-week overview of Wells Fargo, and prepares you to develop, implement, calibrate, or validate quantitative models. The program is designed to provide you a blend of technical, business, and professional developmental opportunities that incorporates real-world experiences and prepares you for a career at Wells Fargo.  Based on individual achievement, those who excel in the internship program may receive an offer for full-time employment. 

There are two tracks:   

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

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

Tracks will be designated and assigned to the candidate upon reviewing application. 

Placement group includes:  

Artificial Intelligence Machine Learning Model Development Center 

Traded Products Model Development  

Risk Modeling Group – Forecast Model Development 

Risk Modeling Group – Decision Model Development 

Market and Counterparty Risk Analytics  

Mortgage Model Development Center 

Model Risk Management  

Consumer Model Development Center 

In this role, you will:

Perform mathematical model development and validation under the direction of experienced team members using Python, R, C++, SAS, SQL or other programming languages 

Be part of a team that provides solutions to business needs and improves risk management processes 

Communicate solutions to stakeholders through verbal presentations and written documentation 

Stay up to speed on industry challenges and new and innovative modeling techniques used across Wells Fargo to solve business problems or enhance business capabilities 

What you will experience:  

Spend your Intern Induction Week at an offsite location 

Structured and engaging onboarding experience  

Speaker series with Wells Fargo senior leaders 

Professional development opportunities 

Networking and engaging with peers 

On-the-job experiences contributing to strategic business goals 

Ideal candidate for this role:

Ability to communicate concepts and ideas, both verbally and written, and explain technical material to a non-technical audience  

For the Capital Markets Track : Experience and demonstrated knowledge in mathematical and numerical methods including Monte Carlo methods, differential equations, linear algebra, applied probability, and statistics  

For the Risk Analytics & Decision Science Track : Experience and demonstrated first-hand knowledge in a number of these areas: data analysis, statistical modeling, machine learning/AI models, data management, and computing 

Energetic self-starter who proactively takes initiative, remains curious and has a genuine interest in learning and growth.

Program Dates: June – August,2025 (10 weeks, 40 hours per week) 

Program Location:  Charlotte, NC 

Required Qualifications:

6+ months of work experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

Desired Qualifications:

Currently pursuing a PhD degree with an expected graduation date between December 2025 – or later 

Currently pursuing a PhD degree in with emphasis on Statistics, Mathematics, Computer Science, Economics, Physics, Quantitative Finance, Operations Research, Data Science, Engineering or related quantitative field or a related quantitative field  

Excellent computer programing skills and use of statistical software packages such as Python, R, SAS, SQL, Spark, Java, and C++  

Excellent verbal, written, and interpersonal communication skills 

Involvement in extracurricular enrichment activities through one or more of the following: volunteerism, student organization involvement, study abroad program(s), leadership position(s), non-profit involvement 

Intermediate Microsoft Office (Word, Excel, Outlook, and PowerPoint) skills 

#earlycareers

How to Apply: 

The 2025 Summer Internship Quantitative Analytics Development Program (PhD) will be accepting applications through the Wells Fargo Career website.  

To search and apply for open positions in this program, please go to https://www.wellsfargo.com/careers/ and click on “Search Jobs”.  

Search by Job ID #: R-384891 or by keywords, “2025 Summer Internship Quantitative Analytics Development Program – PhD” and follow the instructions to complete your application. 

We Value Diversity  

At Wells Fargo, we believe in diversity and inclusion in the workplace; accordingly, we welcome applications for employment from all qualified candidates, regardless of race, color, gender, national origin, religion, age, sexual orientation, gender identity, gender expression, genetic information, individuals with disabilities, pregnancy, marital status, status as a protected veteran or any other status protected by applicable law. 

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Wells Fargo

2025 summer internship quantitative analytics program – early careers phd.

• Share This: Share 2025 Summer Internship Quantitative Analytics Program – Early Careers PhD on Facebook Share 2025 Summer Internship Quantitative Analytics Program – Early Careers PhD on LinkedIn Share 2025 Summer Internship Quantitative Analytics Program – Early Careers PhD on X

About this role:

Wells Fargo is seeking talent to join the Summer Internship Quantitative Analytics Program (PhD) – Early Careers.  Learn more about the career areas and lines of business at wellsfargojobs.com  

Program Overview 

The internship program is a ten-week overview of Wells Fargo, and prepares you to develop, implement, calibrate, or validate quantitative models. The program is designed to provide you a blend of technical, business, and professional developmental opportunities that incorporates real-world experiences and prepares you for a career at Wells Fargo.  Based on individual achievement, those who excel in the internship program may receive an offer for full-time employment. 

There are two tracks:   

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

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

Tracks will be designated and assigned to the candidate upon reviewing application. 

Placement group includes:  

Artificial Intelligence Machine Learning Model Development Center 

Traded Products Model Development  

Risk Modeling Group – Forecast Model Development 

Risk Modeling Group – Decision Model Development 

Market and Counterparty Risk Analytics  

Mortgage Model Development Center 

Model Risk Management  

Consumer Model Development Center 

In this role, you will:

Perform mathematical model development and validation under the direction of experienced team members using Python, R, C++, SAS, SQL or other programming languages 

Be part of a team that provides solutions to business needs and improves risk management processes 

Communicate solutions to stakeholders through verbal presentations and written documentation 

Stay up to speed on industry challenges and new and innovative modeling techniques used across Wells Fargo to solve business problems or enhance business capabilities 

What you will experience:  

Spend your Intern Induction Week at an offsite location 

Structured and engaging onboarding experience  

Speaker series with Wells Fargo senior leaders 

Professional development opportunities 

Networking and engaging with peers 

On-the-job experiences contributing to strategic business goals 

Ideal candidate for this role:

Ability to communicate concepts and ideas, both verbally and written, and explain technical material to a non-technical audience  

For the Capital Markets Track : Experience and demonstrated knowledge in mathematical and numerical methods including Monte Carlo methods, differential equations, linear algebra, applied probability, and statistics  

For the Risk Analytics & Decision Science Track : Experience and demonstrated first-hand knowledge in a number of these areas: data analysis, statistical modeling, machine learning/AI models, data management, and computing 

Energetic self-starter who proactively takes initiative, remains curious and has a genuine interest in learning and growth.

Program Dates: June – August,2025 (10 weeks, 40 hours per week) 

Program Location:  Charlotte, NC 

Required Qualifications:

6+ months of work experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

Desired Qualifications:

Currently pursuing a PhD degree with an expected graduation date between December 2025 – or later 

Currently pursuing a PhD degree in with emphasis on Statistics, Mathematics, Computer Science, Economics, Physics, Quantitative Finance, Operations Research, Data Science, Engineering or related quantitative field or a related quantitative field  

Excellent computer programing skills and use of statistical software packages such as Python, R, SAS, SQL, Spark, Java, and C++  

Excellent verbal, written, and interpersonal communication skills 

Involvement in extracurricular enrichment activities through one or more of the following: volunteerism, student organization involvement, study abroad program(s), leadership position(s), non-profit involvement 

Intermediate Microsoft Office (Word, Excel, Outlook, and PowerPoint) skills 

#earlycareers

How to Apply: 

The 2025 Summer Internship Quantitative Analytics Development Program (PhD) will be accepting applications through the Wells Fargo Career website.  

To search and apply for open positions in this program, please go to https://www.wellsfargo.com/careers/ and click on “Search Jobs”.  

Search by Job ID #: R-384891 or by keywords, “2025 Summer Internship Quantitative Analytics Development Program – PhD” and follow the instructions to complete your application. 

We Value Diversity  

At Wells Fargo, we believe in diversity and inclusion in the workplace; accordingly, we welcome applications for employment from all qualified candidates, regardless of race, color, gender, national origin, religion, age, sexual orientation, gender identity, gender expression, genetic information, individuals with disabilities, pregnancy, marital status, status as a protected veteran or any other status protected by applicable law. 

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Senior Quantitative Finance Analyst

Job Description:

Job Title: Senior Quantitative Finance Analyst

Corporate Title: Up to director

Location: Paris

Company Overview:

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection.  Responsible Growth is how we run our company and how we deliver for our clients, teammates, communities and shareholders every day.

One of the keys to driving Responsible Growth is being a great place to work for our teammates around the world. We’re devoted to being a diverse and inclusive workplace for everyone. We hire individuals with a broad range of backgrounds and experiences and invest heavily in our teammates and their families by offering competitive benefits to support their physical, emotional, and financial well-being.

Bank of America believes both in the importance of working together and offering flexibility to our employees. We use a multi-faceted approach for flexibility, depending on the various roles in our organization.

Working at Bank of America will give you a great career with opportunities to learn, grow and make an impact, along with the power to make a difference. Join us!

Location Overview:

Based in the 8th arrondissement and within the Parisian Golden Triangle, our beautiful Art-Deco era office benefits from luxury modern workspaces, an abundance of natural light and is easily accessible by public transport.

The group is a multi-national team within Enterprise Model Risk primarily based in New York and London. It covers all aspects of model validation and model risk management of front office Credit/Funding Value Adjustment (XVA) models, margin models, and counterparty credit risk (CCR) models including counterparty Internal Method Models (IMM).

Role Description:

Key responsibilities include supporting the development of new models, analytic processes, or system approaches, creating technical documentation for related activities, and working with Technology staff in the design of systems to run models developed. Job expectations may include the ability to influence strategic direction, as well as develop tactical plans.

Responsibilities:

• Performing end-to-end market risk stress testing including scenario design, scenario implementation, results consolidation, internal and external reporting, and assessing stress scenario results to better understand key drivers.
• Supporting the planning related to setting quantitative work priorities in line with the bank’s overall strategy and prioritization.
• Identifying continuous improvements through reviews of approval strategies on relevant model development or model validation tasks, critical feedback on technical documentation, and effective challenges on model development/validation.
• Introducing and providing methodological and technical guidance to effectively influence the strategic direction and tactical approaches of development/validation projects and identify areas of potential risk.
• Working closely with model stakeholders regarding communication of submission and validation outcomes.
• Performs statistical assessments on large datasets and interprets results using both qualitative and quantitative approaches.
• Prepare validation report and technical documents for the model being validated.
• Work closely with the model stakeholders (business, Market Risk, Finance/GVG and other control functions) with respect to compensating controls of the models and communication of validation outcomes. 
• Maintain a sub-portfolio of model inventory and perform annual model reviews, on-going monitoring reviews, Required Actions Items closure and etc. 
• Participate in MRM engagements with regulatory bodies.

What we are looking for:

• You will possess a PhD in quantitative fields such as mathematics, statistics, physics, econometrics or computer science OR have a Masters' degree in same fields with seasoned industry experience.
• In depth understanding of financial mathematics including stochastic calculus and probability theory, as well as derivative pricing and risk models including interest rates and credit risk modelling.
• Prior knowledge of financial derivatives, OTC trading and hedging, collateral management, capital management, bank’s operations and regulatory requirements
• Experience in IMM Basel Capital requirements and European CRR 
• Coding ability in Python, R, C++ is a plus

Skills that will help:

• Predisposed to critical thinking, intellectually curious, detailed-oriented, well-organized, quick learning.  
• Good communication skills (both written and verbal).
• Experience in counterparty modelling is a plus.

Benefits of working at Bank of America:

•           Competitive retirement plan in addition to State plans

•           Mandatory Medical Plan provided by the bank as a top-up to Social Security health benefits for you and your family.

•           Employees and family members can receive free health advice 24/7 through a range of English and French speaking medical providers

•           Life and disability insurance

•           Time Savings Account (‘CET’) to save some of your leave days, cash them out or use them later

•           Reimbursement of 50% of your commuter pass

•           Nursery scheme for children under age 3

•           Works council benefits

•           Access to an Employee Assistance Programme for confidential support and help for everyday matters

•           Opportunity to give back to your community, develop new skills and work with new groups of people by volunteering with local charities

•           Opportunity to receive free entry to arts exhibitions sponsored by Bank of America in Paris

Bank of America:

Good conduct and sound judgment is crucial to our long term success. It’s important that all employees in the organisation understand the expected standards of conduct and how we manage conduct risk. Individual accountability and an ownership mind-set are the cornerstones of our Code of Conduct and are at the heart of managing risk well.

We are an equal opportunities employer and ensure that no applicant is subject to less favourable treatment on the grounds of sex, gender identity or gender reassignment, marital or civil partner status, race, religion or belief, colour, nationality, ethnic or national origins, age, sexual orientation, pregnancy or maternity, socio-economic background, responsibility for dependants or physical or mental disability. The Bank selects candidates for interview based on their skills, qualifications and experience.

We strive to ensure that our recruitment processes are accessible for all candidates and encourage any candidates to tell us about any adjustment requirements.

Hours Per Week:

Weekly Schedule:

Referral Bonus Amount:

Learn more about this role

JR-24027332

Manages People:

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Wells Fargo

2025 summer internship quantitative analytics program – early careers phd.

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About this role:

Wells Fargo is seeking talent to join the Summer Internship Quantitative Analytics Program (PhD) – Early Careers.  Learn more about the career areas and lines of business at wellsfargojobs.com  

Program Overview 

The internship program is a ten-week overview of Wells Fargo, and prepares you to develop, implement, calibrate, or validate quantitative models. The program is designed to provide you a blend of technical, business, and professional developmental opportunities that incorporates real-world experiences and prepares you for a career at Wells Fargo.  Based on individual achievement, those who excel in the internship program may receive an offer for full-time employment. 

There are two tracks:   

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

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

Tracks will be designated and assigned to the candidate upon reviewing application. 

Placement group includes:  

Artificial Intelligence Machine Learning Model Development Center 

Traded Products Model Development  

Risk Modeling Group – Forecast Model Development 

Risk Modeling Group – Decision Model Development 

Market and Counterparty Risk Analytics  

Mortgage Model Development Center 

Model Risk Management  

Consumer Model Development Center 

In this role, you will:

Perform mathematical model development and validation under the direction of experienced team members using Python, R, C++, SAS, SQL or other programming languages 

Be part of a team that provides solutions to business needs and improves risk management processes 

Communicate solutions to stakeholders through verbal presentations and written documentation 

Stay up to speed on industry challenges and new and innovative modeling techniques used across Wells Fargo to solve business problems or enhance business capabilities 

What you will experience:  

Spend your Intern Induction Week at an offsite location 

Structured and engaging onboarding experience  

Speaker series with Wells Fargo senior leaders 

Professional development opportunities 

Networking and engaging with peers 

On-the-job experiences contributing to strategic business goals 

Ideal candidate for this role:

Ability to communicate concepts and ideas, both verbally and written, and explain technical material to a non-technical audience  

For the Capital Markets Track : Experience and demonstrated knowledge in mathematical and numerical methods including Monte Carlo methods, differential equations, linear algebra, applied probability, and statistics  

For the Risk Analytics & Decision Science Track : Experience and demonstrated first-hand knowledge in a number of these areas: data analysis, statistical modeling, machine learning/AI models, data management, and computing 

Energetic self-starter who proactively takes initiative, remains curious and has a genuine interest in learning and growth.

Program Dates: June – August,2025 (10 weeks, 40 hours per week) 

Program Location:  Charlotte, NC 

Required Qualifications:

6+ months of work experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

Desired Qualifications:

Currently pursuing a PhD degree with an expected graduation date between December 2025 – or later 

Currently pursuing a PhD degree in with emphasis on Statistics, Mathematics, Computer Science, Economics, Physics, Quantitative Finance, Operations Research, Data Science, Engineering or related quantitative field or a related quantitative field  

Excellent computer programing skills and use of statistical software packages such as Python, R, SAS, SQL, Spark, Java, and C++  

Excellent verbal, written, and interpersonal communication skills 

Involvement in extracurricular enrichment activities through one or more of the following: volunteerism, student organization involvement, study abroad program(s), leadership position(s), non-profit involvement 

Intermediate Microsoft Office (Word, Excel, Outlook, and PowerPoint) skills 

#earlycareers

How to Apply: 

The 2025 Summer Internship Quantitative Analytics Development Program (PhD) will be accepting applications through the Wells Fargo Career website.  

To search and apply for open positions in this program, please go to https://www.wellsfargo.com/careers/ and click on “Search Jobs”.  

Search by Job ID #: R-384891 or by keywords, “2025 Summer Internship Quantitative Analytics Development Program – PhD” and follow the instructions to complete your application. 

We Value Diversity  

At Wells Fargo, we believe in diversity and inclusion in the workplace; accordingly, we welcome applications for employment from all qualified candidates, regardless of race, color, gender, national origin, religion, age, sexual orientation, gender identity, gender expression, genetic information, individuals with disabilities, pregnancy, marital status, status as a protected veteran or any other status protected by applicable law. 

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