scientific computing phd positions

Ph.D. in Scientific Computing

This program is intended for University of Michigan Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their doctoral studies. A firm knowledge of the scientific discipline is essential.

This is not a stand-alone degree; it is a joint degree program . Students must be accepted into the Ph.D. program of a home department at the University of Michigan. The actual degree name will have “…and Scientific Computing” appended to the the normal title, e.g., “Ph.D. Degree in Aerospace Engineering and Scientific Computing.”

Students in the Scientific Computing degree program come from many different disciplines. Our current enrollment exemplifies the breadth of departments, schools, and colleges represented by our Ph.D. students.

Students may enroll in the program after having completed one term in their home Ph.D. department. We recommend applying prior to being promoted to candidacy status, but can often accommodate students later in their degree progress.

Please contact MICDE at [email protected] if you have any questions about the Ph.D. in Scientific Computing.

Academic Requirements

Application procedures.

Current Students

Tracking Progress

Funding Resources

Current Enrollment

Students must complete the normal doctoral requirements of their home departments, as well as additional requirements in scientific computing. The specific requirements are:

Non-exhaustive examples of course selections for various departments can be seen on our  Example Course Choices page.

Group I Courses

Twenty-four (24) credit hours of coursework toward your home degree. You must complete your home degree requirements in order to receive the Ph.D. in Scientific Computing. It cannot be earned on its own. Group I may overlap with groups II or III. 

Group II Courses

Nine (9) credit hours of approved courses in scientific computing methodologies.

Group III Courses

Nine (9) credit hours of approved courses in computational science and applications in scientific computing outside the home department  (this typically includes courses in computer science, parallel algorithms, advanced computer architectures, computational fluid dynamics, or other courses in scientific computation not offered by a student’s home department).

Committee Composition

An emphasis on scientific computing reflected in doctoral thesis and doctoral committee composition. At least one faculty member on your committee should be an expert in scientific computing, affiliated with MICDE  or  MIDAS .

Demonstration of Understanding

Preliminary/Qualifying Exam Question: You must answer at least one question related to scientific computing during your department’s preliminary or qualifying examination.  If you join the program after having completed your qual/prelim, you can still use this option if you were asked a question related to computational methods or applications during your qual/prelim.  The student’s advisor or a MICDE  or  MIDAS -affiliated member of the committee must then email MICDE to confirm that this requirement is complete.

If the format of your PhD program’s preliminary/qualifying examination cannot accommodate this requirement, or if you are beyond this stage at the point of joining the program and were not asked a question on your prelim/qual, you have the following option to complete the Demonstration of Understanding requirement:

Literature Review: A 3-5 page critical assessment of previous research that has been done in your research area, specifically the scientific computing/computational aspect of your research problem.  This must be submitted to [email protected]  for review 2-4 semesters before your dissertation defense.

If you have any questions about fulfilling the Demonstration of Understanding requirement, please email [email protected] .

For Faculty:

Please send an email to [email protected] describing the scientific computing-related question that was asked during the examination and acknowledging that the student answered the question satisfactorily.

Ph.D. Seminar

If you enrolled in the Ph.D. in Scientific Computing in or after January 2022 , you are required to present at least once before graduation in the Ph.D. Student Seminar Series . Before presenting, you are strongly encouraged to attend as many sessions of the the Ph.D. Seminar Series as you can, from students in your department and outside it. The Ph.D. Student Seminar Series is an opportunity to learn how to simplify your explanation of your research problems and methods in order to talk about them to colleagues outside of your lab or your home department, which will help you prepare for future job searches.

Sign up to present in 2023-2024 on the MICDE Ph.D. Student Seminar Sign-Up form .

Students are expected to work closely with their academic advisors and with MICDE to develop a plan to meet these requirements.

• Talk to your academic advisor about your interest in the Ph.D. in Scientific Computing. Your department must approve your enrollment in the program.
• Submit the Course Audit form . You don’t have to have a full plan in place before filling out the Course Audit form, but please spend some time considering each of the questions and put your answers in the formats requested.
• After the MICDE program administrator checks your Audit Form and transcript, they will contact you to schedule an advising session with an MICDE Management & Education Committee faculty member. During the session, you, the faculty member, and the MICDE program administrator will finalize your plan to meet the requirements of the Ph.D. in Scientific Computing.
• After your advising session, you can apply to the Ph.D. in Scientific Computing . In order to apply, you must complete the Rackham Application Form , have it signed by your department, and submit it to [email protected] . You are not enrolled in the program unless you have completed this step.

Questions? Contact the Program Administrator at [email protected] .

Eligibility

This is not a stand-alone degree; it is a   joint degree program . Students must be accepted into the Ph.D. program of a home department at the University of Michigan-Ann Arbor.

Enrollment Deadlines

Students are enrolled on a rolling basis as they apply.

Information for Current Students

Please contact the program administrator ( [email protected] ) for all questions related to the Ph.D. in Scientific Computing.

We track students’ progress through the Ph.D. in Scientific Computing Web Progress Form . Your Web Progress Form is created after the advising session, and is accessible by prospective students as well as those who are enrolled. Every summer we will reach out to students to update their Web Progress Form with anything that has changed since the previous summer.

Updating the Web Progress Form

Please plan to update your Web Progress Form each summer with new information, including:

• If you answered questions about scientific computing in your quals/prelims and your Web Progress Form does not reflect this, please describe the questions in the Candidacy Status section.
• If you have formed your doctoral committee, please list the members in the Committee Information section.
• If you have made any changes to the courses you took or plan to take to fulfill requirements for the Ph.D. in Scientific Computing (including changing courses from “planned” to “completed” once you’ve taken them) please update the Course requirements section.
• If you have made progress in your research that is not yet reflected on your WPF (awards, fellowships, conference presentations, publications, etc.) please update the Research Progress section.
• Please make sure that your current estimated graduation term is listed in the  Future Plans section. This is not set in stone, but helps us to understand where you are in your degree process.

Enrollment Status

Note that each student has one of the following 5 statuses on the Web Progress Form . If you believe the enrollment status listed on your Web Progress Form is incorrect, please email [email protected] .

• Enrolled  ( had an advising session, turned in their application form to MICDE and Rackham has processed the application )
• Prospective  ( had an advising session, but has not yet enrolled ) Please let us know if you are still interested in enrolling in the program so we can finish your enrollment. You can log in to the Web Progress Form to see what courses were discussed in your original advising appointment.
• Leave of Absence  ( you are enrolled in the program, and currently in a leave of absence from your home program ) Please let us know when you return from a leave of absence.
• Graduated  ( you graduated from the program in 2015 or later)
• Discontinued  (you discontinued the Ph.D. in Scientific Computing and/or your home program)

You can view your Web Progress Form at any time. If you want to make any changes to your Web Progress Form outside of the summer window, or if you have any problems with accessing the form, please email [email protected] .

• Confirm that your transcript shows you are enrolled in the PhD in Scientific Computing.  If your transcript doesn’t show your enrollment in the program, please contact the program administrator ( [email protected] ) to find out your status within the program.
• If your transcript shows your enrollment in the Scientific Computing program, please review all the information we have on file for you on the Web Progress Form . In particular, check the Graduation requirements summary section at the top. If any of the boxes are blank or incomplete, please ask the program administrator ( [email protected] ) to review your requirements and confirm that they are complete.
• During the term you want to graduate, please contact the program administrator ( [email protected] ) to let them know so they can process your information.

Don’t forget to add the PhD in Scientific Computing program to the title page of your dissertation! For example:  (Physics and Scientific Computing)

A1: Please see  this list for examples. Note that they are only samples of what other students have done, but they are not the only choices. This degree is extremely individualized, so please email the program administrator ( [email protected] ) for more course information.

Q2: I met with the program director, but I get an error when I try to access the Web Progress Form. What can I do?

A2: Please contact the program administrator ( [email protected] ) to inquire about your status.

Q3: Can I change the courses listed on my form?

A3: Yes, but note that any course changes must be approved by MICDE. Email the program administrator ( [email protected] ) if you have any questions.

Q4: How often are students required to complete the Web Progress Form

A4: We ask students to fill out the form annually, by the end of summer each year.

Q5: What if I want to know if a course is approved before the Annual Form is due?

A5: Please contact the program administrator ( [email protected] ) to initiate the approval process. Once approved, they will record it in your form.

Q6: The form lists my status as “PROSPECTIVE” but I think I should be enrolled. What should I do?

This bar graph represents the numbers of students from different departments at U-M enrolled in the program. Students come from the College of Engineering, School of Kinesiology, College of LSA, Michigan Medicine, College of Pharmacy, Ross Business School, School for Environment and Sustainability, School of Information and the School of Public Health.

Departments include: Aerospace Engineering, Civil and Environmental Engineering, Biomedical Engineering, Chemical Engineering, Climate and Space Sciences and Engineering, Electrical Engineering and Computer Science, Industrial & Operations Engineering, Macromolecular Science & Engineering, Mechanical Engineering, Materials Science & Engineering, Naval Architecture & Marine Engineering, Nuclear Engineering & Radiological Sciences, Applied Physics, Chemistry, Chemical Biology, Earth and Environmental Sciences, Linguistics, Mathematics, Physics, Political Science, Psychology, Biostatistics, Environmental Health Sciences, Epidemiology, Health Behavior & Health Education, Kinesiology, Health Infrastructures & Learning Systems, Neuroscience, Pharmaceutical Sciences, Business and School for Environment and Sustainability.

This list is not exhaustive, and continues to grow.

Ph.D. in Scientific Computing years in existence

Current Ph.D. in Scientific Computing students

Alumni since 1992

History of the Ph.D. in Scientific Computing

Text Version

Faculty Leadership

For all questions about the Ph.D. in Scientific Computing, please email [email protected] .

Karthik Duraisamy

2022 – present

2004 – 2022

Bill Martin

1988 – 2004

Mark your calendar for the MICDE SciFM 2024 Conference on April 2nd & 3rd, 2024!

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School of Computational Science and Engineering

College of computing, ph.d. programs.

For those students looking to build a career in computing research, the School of Computational Science and Engineering offers a range of Ph.D. programs that allow students to work side-by-side with some of the most brilliant researchers and computer scientists in the world. For those looking to join the ranks of academia, we regularly place our doctorate graduates in tenure-track positions in top programs. The School maintains strong, research relationships with companies from Fortune 500 to the latest startups that allow graduates to continue their research in jobs at some of the world’s hottest private sector employers.

Ph.D. in Computational Science and Engineering Interdisciplinary program devoted to the creation, study, and application of computer-based models of natural and engineered systems. 

Ph.D. in Computer Science As a research-oriented degree, the Ph.D. in Computer Science prepares exceptional students for careers at the cutting edge of academia, industry and government. 

Ph.D. in Machine Learning Machine learning builds and learns from both algorithm and theory to understand the world around us and create the tools we need and want.

Ph.D. in Bioinformatics An elite multidisciplinary program sponsored by the College of Computing, the H. Milton Stewart School of Industrial and Systems Engineering, and the School of Mathematics.

Ph.D. in Bioengineering Be a leader through interdisciplinary graduate education and research in areas that improve health and the environment.

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Faculty and researchers.

• David Bindel
• Giulia Guidi
• Bharath Hariharan
• Volodymyr Kuleshov
• Wei-Chiu Ma
• Curran Muhlberger
• David Shmoys
• Alex Townsend
• Charles Van Loan

Related Areas

Applied mathematics.

Scientists and engineers rely more than ever on computer modeling and simulation to guide their experimental and design work. The infrastructure that supports this activity depends critically on the development of new numerical algorithms that are reliable, efficient, and scalable. "Large N" is the hallmark of modern, data-intensive scientific computing and it is a common thread that unifies departmental research in numerical linear algebra, optimization, and partial differential equations.

David Bindel works on numerical linear algebra, numerical methods for data science, and simulating microelectromechanical systems and fusion plasmas. His research involves software design, mathematical analysis and physical modeling.

Anil Damle  works on the development of fast algorithms in applied and computational mathematics that exploit structure coming from underlying physical or statistical models. This includes work in the areas of computational quantum chemistry, numerical linear algebra, and spectral clustering.

Giulia Guidi  works in the field of high-performance computing for large-scale computational sciences (in particular, computational biology). Her research involves the development of algorithms and software infrastructures on parallel machines to accelerate data processing without sacrificing programming productivity and to make high-performance computing more accessible.

The scientific computing group is also active in the Applied Mathematics Ph.D. program, which is part of Cornell's Center for Applied Mathematics . Prospective Ph.D. applicants interested in the mathematical aspects of scientific computing may wish to consider that graduate field as well.

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List of the groups who offer PhD positions in Scientific Computing and its applications

This is a list of research groups and individuals who contribute to the Centre's teaching and research and offer graduate research positions in scientific computing and its applications.

• The Laboratory for Scientific Computing (Cavendish)
• Theory of Condensed Matter (Cavendish)
• Department of Chemistry
• Department of Engineering
• Department of Materials and Metallurgy

If you are interested specifically in the development and applications of Computational Methods for Materials Modelling, please visit the website of the Lennard-Jones Centre  (link opens in a new window).

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Ph.D. in Computational Science

Many of the important problems facing society today can only be solved by teams of individuals from a variety of disciplines. An individual trained in an interdisciplinary environment is an essential member of such a team because he/she can successfully interact with team members and gain an overall understanding of the problem.

However, most doctoral programs in this country have a very narrow focus and consequently, students rarely have the opportunity to understand research themes in other disciplines or even to explore the relevance of their own research to other fields. Since the Department of Scientific Computing (DSC) lies at the intersection of applied mathematics, applied science, computer science and engineering, it has the unique opportunity to train students in areas which cut across disciplines.

Ph.D Program Tracks

The goal of the Ph.D. program in Computational Science is to train graduate students to have extensive knowledge in computational science and to allow the student to acquire expertise in one or more areas of the sciences, mathematics, or engineering. Students may choose to follow the major track in computational science which allows them to specialize in aspects of:

computational mathematics computer science

relevant to computational science or choose to complete one of the following specialized tracks:

Coursework & credits

Since computational science is an interdisciplinary program, students' programs of study can be quite varied. Consequently, we have built in a lot of flexibility into the course requirements for the Ph.D. in Computational Science. The commonality in the coursework is that each student must take the same four computational science courses (Group A courses), which cut across disciplines, as well as a minimum of 9 credit hours in other computational science courses (Group B courses) plus 9 additional credit hours selected from existing departmental courses in computer science, engineering, mathematics or an applied science (Group C courses). Each student must also take six seminar hours (details are listed in the Graduate Handbook, section 5.2.2 ).

There are a total of 29 credit hours of coursework that are specified plus a minimum of 24 credits of dissertation hours. Additional credit hours may be obtained through dissertation hours, other computational science courses, or existing courses offered by departments. We do not impose any additional credit hours over the University's residence requirement of twenty-four graduate semester hours credit after completion of the master's degree or after thirty semester hours of graduate work. A specialization is obtained by completing a minimum of 9 credit hours from courses in the discipline approved by the student's supervisory committee. More information on the courses below can be found in our list of courses .

The required core courses (Group A) consist of:

• Introduction to Scientific Programming (3 credits)
• Applied Computational Science I (4 credits)
• Applied Computational Science II (4 credits)
• Parallel Programming, Algorithms and Architectures (3 credits)

The elective core courses (Group B) consist of courses such as:

• Monte Carlo/Markov Chain Simulations
• Survey of Numerical PDEs
• Computational Space Physics
• Programming Skills for Computational Biology and Bioinformatics
• Computational Evolutionary Biology
• Introduction to Bioinformatics
• Computational Finite Element Methods
• Numerical Methods for Stochastic Differential Equations
• Numerical Methods for Earth and Environmental Sciences
• Molecular Dynamics
• Numerical Linear Algebra
• Data mining
• Visualization
• Verification and Validation in Computational Science

Note: Student should select a minimum of three courses from Group B, which are approved by his/her supervisory committee.

Application and Information

Students applying to this program should have earned a bachelor's or master's degree in an applied science, mathematics, computer science or engineering and possess a keen interest in computational science. The DSC expect that graduates of this program will be prepared to seek employment in academic, industrial, or laboratory settings.

You can apply for the graduate programs online . Please start your application process early.

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Found 56 PhD jobs

Principal investigator in modeling of plant stress responses.

• Umeå (Kommun), Västerbotten (SE)
• Competitive
• Umeå Plant Science Centre and Integrated Science Lab

Join our multidisciplinary and stimulative research environment as Associate Professor in Modeling of Plant Stress Responses

View details Principal Investigator in Modeling of Plant Stress Responses

• 16 days ago
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Group Leader at Católica Biomedical Research Centre and Assistant or Associate Professor at Católica

• Portugal (PT)
• Commensurate with experience and merit and adhering to institutional norms/national legislation
• Católica Biomedical Research Centre

Group Leader + Assistant/Associate Professor, tenure-track position in Biological and Biomedical Sciences, Data Science, Engineering, related fields.

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Expression of Interest – Marie Skłodowska-Curie Actions – Postdoctoral Fellowships 2024 (MSCA-PF)

• France (FR)
• not relevant
• Plateforme projets européens (2PE) -Bretagne

Academic institutions in Brittany are looking for excellent postdoctoral researchers willing to apply for a Marie S. Curie Postdoctoral Fellowship.

View details Expression of Interest – Marie Skłodowska-Curie Actions – Postdoctoral Fellowships 2024 (MSCA-PF)

• 10 days ago
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Tenure-track Assistant Professor in Ecological and Evolutionary Modeling

• Umeå, Sweden
• Umeå University

Tenure-track Assistant Professor in Ecosystem Ecology linked to IceLab’s Center for modeling adaptive mechanisms in living systems under stress

View details Tenure-track Assistant Professor in Ecological and Evolutionary Modeling

• 12 days ago
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Tenure-Track Assistant Professor to the rank of Associate Professor in computational biology

• Lausanne, Canton of Vaud (CH)
• University of Lausanne (UNIL)

UNIL is a leading international teaching and research institution, with over 5,000 employees and 15,500 students split between its Dorigny campus, ...

View details Tenure-Track Assistant Professor to the rank of Associate Professor in computational biology

• 26 days ago
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Two Professorships (W3) for Digital Health: Non-communicable Disease & Health Information Technology

• Potsdam, Brandenburg (DE)
• Hasso-Plattner-Institut für Digital Engineering gGmbH

We seek Medical Data Scientists at the intersection of computer science and digital health sciences.

View details Two Professorships (W3) for Digital Health: Non-communicable Disease & Health Information Technology

• 36 days ago
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Scientist* Personalized Computational Genomics

• Mainz, Rheinland-Pfalz (DE)
• Market competitive salary
• BioNTech SE

Researcher to join our Personalized Computational Genomics team to support the development of cutting-edge personalized cancer vaccines

View details Scientist* Personalized Computational Genomics

• 45 days ago
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Software Engineer* Personalized Computational Genomics

Computer Scientist to join our Personalized Computational Genomics team to support the development of cutting-edge personalized cancer vaccines

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Postdoctoral Researcher - GLOSSAM College of Arts, Social Sciences & Celtic Studies (010301)

• Galway (City), Connacht (IE)
• University of Galway (NUI Galway)

Postdoctoral Researcher - GLOSSAM, College of Arts, Social Sciences & Celtic Studies, School of Languages, Literatures and Cultures (010301) Applic...

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I-2425 – POST-DOC IN WIRELESS NETWORK SECURITY

• to be discussed
• Luxembourg Institute of Science and Technology (LIST)

Temporary contract | 12 Months | Belval Are you passionate about research? So are we! Come and join usThe Luxembourg Institute of Science and Technolo

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M-2469 – POST DOC IN MODELLING, SIMULATION, AND DESIGN FOR OPTIMAL PULTRUSION PROCESS

• BASED ON EXPERIENCE

Temporary contract | 24 months | Hautcharage Are you passionate about research? So are we! Come and join usThe Luxembourg Institute of Science and Tec

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Junior Group Leader in Computational Chemistry

• Prague (CZ)
• Institute of Organic Chemistry and Biochemistry of the CAS (IOCB Prague)

IOCB Prague welcomes applications for a Junior Group Leader position in computational chemistry with an emphasis on state-of-the-art tools of biomo...

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Doctoral researcher (PhD candidate) in Computer Science

• University of Luxembourg

The University | About us...The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary

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Postdoc in Smart Traverse for Mobile Robotics

• Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg

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PhD Student in Scientific Computing – Uppsala University, Sweden

PhD Student in Scientific Computing: Embark on a research journey in scientific machine learning as a PhD student at Uppsala University. Join the Division of Scientific Computing within the Department of Information Technology, a leading research hub in scientific computing, and contribute to cutting-edge developments in deep learning and Bayesian methods. This international and collaborative environment offers a unique opportunity to delve into simulation-based inference and work on impactful scientific problems.

PhD Student in Scientific Computing – Scientific Machine Learning

Designation: PhD Student

Research Area: Scientific Computing, Scientific Machine Learning, Deep Learning, Bayesian Methods

Location: Uppsala, Sweden

Eligibility/Qualification:

• Master’s degree (second-cycle) in computer science, computational science, applied mathematics, engineering physics, machine learning, data science, or a related field.
• Strong interest in optimization, machine learning, and Bayesian inference.
• Proficiency in oral and written English.
• Excellent study results and programming skills (preferably in Python).
• Additional qualifications in subjects like optimization, probabilistic machine learning, linear algebra, and deep learning are valued.

Job Description:

• Devote time primarily to graduate education.
• Conduct research on learning from noisy datasets using deep learning and Bayesian methods.
• Work on simulation-based inference for expressive, informative features from scientific data.
• Collaborate with the Scientific Machine Learning group, participate in ongoing projects, and contribute to the development of principled foundations in the field.
• Potential involvement in teaching and departmental activities, up to 20% of the time.

How to Apply: Interested candidates must submit:

• Statement of motivation (up to 2 pages) explaining why they are the right candidate for the position.
• Degrees and transcript of records with grades (translated to English or Swedish).
• Master’s thesis, publications, or other relevant documents.
• References with contact information (up to two letters of recommendation).

Applicants meeting entry requirements are strongly encouraged to apply. Specify the earliest possible starting date.

Last Date for Apply: April 2, 2024

Note: Applications will be reviewed on a rolling basis. Ensure timely submission.

For further information, contact:

• Assistant Professor Prashant Singh, [email protected]
• Head of Division Emanuel Rubensson, [email protected]

Join us at Uppsala University, Sweden, in pushing the boundaries of scientific machine learning and contributing to transformative research in scientific computing.

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ISTA GRADUATE SCHOOL

The Graduate School Office (GSO) at ISTA provides support, development opportunities, funding and advocacy for current and prospective PhD students.

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Our PhD students’ and faculty’s dedication to excellence and innovation shapes the future of science.

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Fully funded PhD positions in Astronomy, Biology, Computer Science, Chemistry & Materials, Data Science & Scientific Computing, Earth Science, Mathematics, Neuroscience, and Physics

Job Information

Offer description.

The Institute of Science and Technology Austria - ISTA is looking for highly qualified candidates with Bachelor’s or Master’s degrees to apply for the PhD program. We offer  fully-funded PhD positions in Astronomy, Biology, Computer Science, Chemistry & Materials, Data Science & Scientific Computing, Earth Science, Mathematics, Neuroscience, and Physics , in a world-class research environment on the outskirts of Vienna.

 In 2019, ISTA was ranked #3 in the world-wide Nature Index ranking (normalized). In 2018, ISTA was ranked within the top ten of Nature Index Rising Stars “Top 30 academic institutions under 30” list.

All students are offered  5-year contracts , they receive internationally competitive salaries, and full social security coverage. There is also financial support for students for attending scientific conferences and workshops.

The  application deadline is January 8, 2024  for a PhD start date in September 2024.

Requirements

ISTA welcomes students with different academic backgrounds who can bring their skills and experiences to the scientific setting.

Students with a  Bachelor’s or Master’s degree  in Astronomy, Biology, Computer Science, Chemistry & Materials, Data Science & Scientific Computing, Earth Science, Mathematics, Neuroscience, and Physics are encouraged to apply.

ISTA processes applications for its PhD program once a year. To  apply , you have to create an online account, which will allow you to track your application through its various stages. 

WHAT APPLICATION MATERIALS ARE REQUIRED?

We ask you to upload the following documents to the application portal:

• Statement of purpose  that explains your research experience and your motivation for applying to the ISTA PhD program. It should be 1-2 pages maximum. In your statement, specify up to three research groups you find the most interesting. At this stage you do not need to contact potential supervisors.
• University transcripts and diplomas  of all degree programs and all years you have attended and/or are currently enrolled in (in English and in original language)
• Contact information of 3 referees

All documents have to be submitted in English and copies of university transcripts also in their original language.

Additional Information

• International and Diverse Scientific Community

Our scientists, from professors to postdocs to students, hail from all corners of the world, representing over 80 different nationalities, and building on research and degrees earned from the finest global scientific institutes and universities.

• English-Speaking

We use English throughout the institute as the language of instruction and communication.

• Interdisciplinary

 Our Graduate Program is characterized by modern comprehensive training with a special focus on  interdisciplinarity.

• Cutting-edge Research Training

Students work closely with outstanding faculty within small research groups and have access to first-rate facilities. Students spend the first year completing coursework and rotations before affiliating with a group.

This first phase of exploration allows students to be sure that the research topic, as well as the research group, are the best match for them before they embark on in-depth thesis research in the second phase.

• Entry with a Bachelor's or Master's Degree

We welcome students with a bachelor’s or a master’s in science to apply for our PhD program.

In all fields of research,  funding  is a very important topic. ISTA relies on diverse funding sources to support all its researchers. The Grant Office is there to support ISTA scientists who would like to apply for external funding. 

All PhD students are offered 5-year contracts . All students making reasonable progress are fully funded until the time of their thesis defense.

Students also receive financial support to attend scientific conferences and workshops during their studies.

Our distinguished alumni have gone on to successful  careers  after their PhD studies at ISTA.

PhD students receive support through our mentorship and career development programs which focus on training in transferable skills for academia and beyond. Throughout their studies, PhD students are supported by Career Services, which provides career development advice and training.

There are also regular talks organized by the XISTA Innovation , which aim to support researchers in the commercial development and use of their research results.

Join our Virtual Student Open Day on 16 November!

Register now

The Virtual Open Day is a great opportunity to explore our programs and campus virtually, wherever you are. The program includes a range of live sessions from the Graduate School Office, academics and students. You can find out about the PhD program, the ISTA research experience, the support services, the ISTA campus, and much more by attending one of the talks.

Work Location(s)

Where to apply.

• Ph.D. in Mathematics
• Ph.D. in Atmosphere Ocean Science
• M.S. in Mathematics

M.S. in Scientific Computing

• M.S. in Mathematics in Finance
• Non-Degree Study
• M.S. at Tandon School of Engineering
• Current Students

The departments of mathematics and computer science at NYU's Courant Institute of Mathematical Sciences offer a master's degree in scientific computing. The program provides broad yet rigorous training in areas of mathematics and computer science related to scientific computing. It aims to prepare people with the right talents and background for a technical career doing practical computing.

The program accommodates both full-time and part-time students, with most courses meeting in the evening. The masters program focuses on computational science, which includes modeling and numerical simulation as used in engineering design, development, and optimization. While data science is an increasingly important aspect of computational science, this program is distinct and different from the recently-created  Masters of Science in Data Science  within the  NYU Center for Data Science . Students specifically interested in data science are encouraged to apply to that program instead.

See frequently-asked questions regarding the impact of the Covid-19 pandemic on MS programs.

Scientific Computing: Overview

Scientific computing is an indispensable part of almost all scientific investigation and technological development at universities, government laboratories, and within the private sector. Typically a scientific computing team consists of several people trained in some branch of mathematics, science, statistics, or engineering. What is often lacking is expertise in modern computing tools such as visualization, modern programming paradigms, and high performance computing. The master's program in scientific computing aims to satisfy these needs, without omitting basic training in numerical analysis and computer science. Many graduates of this program work at technologically advanced institutions, especially in research and development, where their skills and experience complement those without interdisciplinary degrees. The program is also open to students who will go on to pursue doctoral studies in computer science, mathematics, or statistics.

The master's program in scientific computing focuses on the mathematics and computer science related to advanced computer modeling and simulation. The program is similar in structure to terminal master's programs in engineering, combining classroom training with practical experience. The coursework ranges from foundational mathematics and fundamental algorithms to such practical topics as data visualization and software tools. Electives encourage the exploration of specific application areas such as mathematical and statistical finance, applications of machine learning, fluid mechanics, finite element methods, and biomedical modeling. The program culminates in a master's project, which serves to integrate the classroom material.

Admission Requirements

The program requires least three semesters of Calculus (including  multivariate calculus ), as well as linear algebra . Experience with  programming in a high-level language  (e.g., Java, C, C++, Fortran, Python) as well as  data structures  and algorithms , equivalent to a first-year sequence in computer science,  is also required. It is highly desirable that applicants have undergraduate major or significant experience in mathematics, a quantitative science or engineering, or economics.

Applicants are strongly recommended to submit their applications by February 15 for Fall semester enrollment. The final application deadline for Fall semester enrollment is May 1. The program admits students both on a full-time and on a part-time basis. The application process takes place online via the Graduate School of Arts and Sciences; please visit the  Graduate School Admissions site .

For more information, please contact us at

[email protected] Phone:  (212) 998-3238 Fax: (212) 995-4121

Degree Requirements

A candidate for a master's degree in scientific computing must complete a number of requirements. .

Computing Facilities

The Courant Institute makes available for graduate training and coursework a network of workstations maintained by systems administrators. All graduate students have computer accounts for the duration of their studies. NYU also runs a high-performance computing center with both shared-memory and distributed-memory computers.

Many members of the departments of mathematics and computer science have research interests bearing on scientific computing. The list includes:

Marsha J. Berger . B.S. 1974, Binghamton; M.S. 1978, Ph.D. 1982, Stanford. Research interests: computational fluid dynamics, adaptive mesh refinement, parallel computing.

Yu Chen . B.S. 1982, Tsinghua; M.S. 1988, Ph.D. 1991, Yale. Research Interests: numerical scattering theory, ill-posed problems, scientific computing.

Aleksandar Donev . B.S. 2001, Michigan State; Ph.D. 2006, Princeton. Research interests: multi-scale methods, fluctuating hydrodynamics, coarse-grained particle methods, jamming and packing.

Davi Geiger . B.S. 1980, Pontifica (Brazil); Ph.D. 1990, MIT. Research interests: computer vision, information theory, medical imaging, and neuroscience.

Jonathan B. Goodman . B.S. 1977, MIT; Ph.D. 1982, Stanford. Research interests: numerical analysis, fluid dynamics, computational physics, partial differential equations.

Leslie Greengard . B.A. 1979, Wesleyan; M.S. 1987, Yale School of Medicine; Ph.D. 1987, Yale. Research interests: scientific computing, fast algorithms, potential theory.

Yann LeCun . B.S. 1983, ESIEE (Paris); D.E.A. 1984, Ph.D. 1987, Pierre and Marie Curie University (Paris). Research interests: machine learning.

Andrew Majda . B.S. 1970, M.S. 1971, Ph.D. 1973, Stanford. Research interests: modern applied mathematics, atmosphere/ocean science, turbulence, statistical physics.

Bhubaneswar Mishra . B.S. 1980, India Institute of Technology, Kharagpur; M.S. 1982, Ph.D. 1985, Carnegie-Mellon. Research interests: robotics, mathematical and theoretical computer science.

Michael L. Overton . B.S. 1974, British Columbia; M.S. 1977, Ph.D. 1979, Stanford. Research interests: numerical linear algebra, optimization, linear and semidefinite programming.

Kenneth Perlin . B.A. 1979, Harvard; M.S. 1984, Ph.D. 1986, NYU. Research interests: computer graphics, simulation, computer-human interfaces, multimedia.

Charles S. Peskin .  B.A. 1968, Harvard; Ph.D. 1972, Yeshiva. Research interests: physiology, fluid dynamics, numerical methods.

Aaditya V. Rangan . B.A. 1999, Dartmouth; Ph.D. 2003, Berkeley. Research interests: large-scale scientific modeling of physical, biological, and neurobiological phenomena.

Tamar Schlick . B.S. 1982, Wayne State; M.S. 1984, Ph.D. 1987, NYU. Research interests: mathematical biology, numerical analysis, computational chemistry.

Michael J. Shelley . B.S. 1981, Colorado; M.S. 1984, Ph.D. 1985, Arizona. Research interests: scientific computation, fluid dynamics, neuroscience.

Eero Simoncelli . B.A. 1984, Harvard; M.S. 1988, Ph.D. 1993, MIT. Research interests: image processing, computational neuroscience, computer vision.

Esteban Tabak . Bach. 1988, Buenos Aires; Ph.D. 1992, MIT. Research interests: fluid dynamics, conservation laws, optimization and data analysis.

Olof B. Widlund . C.E. 1960, Tekn. L. 1964, Technology Institute, Stockholm; Ph.D. 1966, Uppsala. Research interests: numerical analysis, partial differential equations, parallel computing.

Margaret H. Wright . B.S. 1964, M.S. 1965, Ph.D. 1976, Stanford. Research interests: mathematical optimization, numerical methods, nonlinear programming.

Denis Zorin . B.S. 1991, Moscow Institute of Physics and Technology; M.S. 1993, Ohio State; Ph.D. 1997, Caltech. Research interests: computer graphics, geometric modeling, subdivision surfaces, multiresolution surface representations, perceptually based methods for computer graphics.

Miranda Holmes-Cerfon . B.S. 2005 University of British Columbia, PhD 2010 NYU. Research interests: soft-matter physics, fluid dynamics, oceanography, stochastic methods. 

Antoine Cerfon . B.S. 2003, M.S. 2005 Ecole des Mines de Paris, PhD 2010 MIT. Research interests: Computational plasma physics, multi-scale methods, fast algorithms.

Dimitris GIannakis . MSci 2001 Cambridge, PhD 2009 Chicago. Research interests: geometrical data analysis, statistical modeling, climate dynamics.

Academic Standards

To register for courses, students must maintain good academic standing, fulfilling the following requirements:

• Students must maintain an average of B or better over their first twelve credits. Students who fail to achieve this cannot continue in the master's program.
• Students cannot obtain a master's degree unless they have maintained an overall average of B or better. Students at risk of failing to meet this requirement receive a warning letter from the department.
• Students cannot obtain more than four no-credit grades, withdrawals, or unresolved incomplete grades during their academic tenure, and no more than two such grades in the first six courses for which they have registered.

Up to two core courses taken elsewhere can earn transfer credit, subject to the normal NYU graduate school restrictions on transfer of credit and the approval of the program director. At least 30 credits must be taken at NYU. 

For further  administrative  information (including applications, transfer of credits, entrance exams, registration for courses, etc.) please contact:

Gehan Abreu De Colon  [email protected]

For further  academic  information (e.g., substituting a course) please contact:

Jonathan B. Goodman Director of the Master's Program in Scientific Computing [email protected]

Revised Fall 2016