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] .

Eric Johnsen

2024 – present

Karthik Duraisamy

2022 – 2024

2004 – 2022

Bill Martin

1988 – 2004

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Found 21 jobs

Faculty positions at the center for machine learning research (cmlr), peking university.

• Beijing, China
• Competitive salary
• Center for Machine Learning Research (CMLR), Peking University

CMLR's goal is to advance machine learning-related research across a wide range of disciplines.

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• 26 days ago
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Unlock Your Potential with a PhD at Scuola Superiore Meridionale

• Naples (IT)
• €19,000 per year
• Scuola Superiore Meridionale

Nestled in the vibrant city of Naples, Italy, SSM is a prestigious institution of higher learning and research

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• 29 days ago
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Global Talent Recruitment of Xinjiang University in 2024

• Wulumuqi city, Ürümqi, Xinjiang Province, China
• Salary and benefits can be contacted by phone with the person in charge.
• Xinjiang University

Recruitment involves disciplines that can contact the person in charge by phone.

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• 56 days ago
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Principal Investigators and Postdoctoral Fellows

• Nanchang, Jiangxi province, China
• The excellent applicants enjoy the special treatment.
• Jiangxi Science & Technology Normal University

Doctoral Recruitment of Jiangxi Science & Technology Normal University

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PhD scholarship in Forecasting Bioplastic Degradability with Machine Learning – DTU Sustain

• Copenhagen, Hovedstaden (DK)
• Competitive
• Technical University of Denmark (DTU)

Are you passionate about creating a sustainable future and pioneering in the fields of bioplastics and machine learning?

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PhD in “Science Learning Spaces as Learning Media”

• Odense, Fyn (DK)
• University of Southern Denmark (SDU)

The PhD project should be relevant to practice in the Danish primary school and/or teacher education.

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PRISMAS Project: PhD Research and Innovation in Synchrotron Methods and Applications in Sweden

• Lund (Stad), Skåne (SE)
• averaging 3000€/month over four years
• Lund University

Are you the next PRISMAS student? PRISMAS – PhD Research and Innovation in Synchrotron Methods and Applications in Sweden – is a new doctoral prog...

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PhD Machine Learning and Simulation-based Analysis Smart Grids

• Luxembourg, Luxembourg (Canton)
• University of Luxembourg

About the SnTSnT is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services. We play an in

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• 12 days ago
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PhD student (f/m/x) in Computational Biology

• Cologne, Nordrhein-Westfalen (DE)
• "-"
• Universitätsklinikum Köln (AöR)

Your future with us Working at the University Hospital Cologne and the Medical Faculty means helping to shape the future - the future of medicine, ...

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• 14 days ago
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Postdoc SDR for Advanced Satellite Communication Techniques

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

About the SnT...SnT is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services. We play an

View details Postdoc SDR for Advanced Satellite Communication Techniques

• 16 days ago
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PhD Candidate in Digital Twins for Mobility Applications

• Luxembourg (Canton), Luxembourg

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PhD Candidate in Autonomous Driving Systems

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Postdoctoral Researchers and PhD Students in Computational Nanoscience

• Jyväskylä (Kaupunki), Keski-Suomi (FI)
• postdoctoral researcher: 44 000 -51 000 EUR / year; PhD student: 30 000 EUR / year
• University of Jyväskylä

Postdoctoral Researcher and PhD student positions in computational nanoscience at University of Jyväskylä

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• 23 days ago
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PhD Project: Microtubule nanomechanics and turnover under cell-like physical constraints

• Max Planck Institute for Multidisciplinary Sciences

The Max Planck Institute for Multidisciplinary Sciences is a leading international research institute of exceptional scientific breadth. With more ...

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• 28 days ago
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PhD Position in DNA Tools for Spatial Biology

• Karolinska Institutet (KI)

Do you want to contribute to top quality medical research? To be a doctoral student means to devote oneself to a research project under supervision of

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PhD Student in Machine learning for radiological precision medicine

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Postdoc and PhD positions in the CRC 1664 - Plant Science • Protein Science • Computer Science

• Halle (Saale), Leipzig, Gatersleben in GERMANY
• Full time and part-time positions according to Entgeldgruppe 13 TV-L
• Collaborative Research Centre CRC 1664 - SNP2Prot

The CRC “Plant Proteoform Diversity” - Bridging the gap from genetic to phenotypic variation, become part of the team as a Postdoc or PhD student

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• 30 days ago
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PhD position in machine learning to predict nitrogen leaching at field level

• Aarhus, Midtjylland (DK)
• Aarhus University (AU)

The PhD position is part of the NITAGRO project that aims to deliver more accurate, field-level predictions of nitrate leaching.

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• 34 days ago
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PhD in Agricultural Ecology and Machine Learning: An Underground Video Tracking System to Charact...

An Underground Video Tracking System to Characterise Novel Earthworm-Plant Interactions

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• 36 days ago
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PhD Position in Photogrammetry and Computer Vision

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

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• 41 days ago
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ICME Doctor of Philosophy

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The Institute for Computational and Mathematical Engineering (ICME), and its predecessor program Scientific Computing and Computational Mathematics, has offered MS and PhD degrees in computational mathematics for over 30 years. Affiliated Faculty conduct groundbreaking research, train and advise graduate students, and provide over 60 courses in computational mathematics and scientific computing at both the undergraduate and graduate level, to the Stanford community.

Doctoral Program

We develop innovative computational and mathematical approaches for complex engineering and scientific problems, attracting talented PhD students from across the globe. Advised in research by more than 50 faculty from 20-plus departments, PhD students are immersed in a wide variety of fields including statistics and data science, machine and deep learning, control, optimization, numerical analysis, applied mathematics, high-performance computing, earth sciences, flow physics, graphics, bioengineering, genomics, economics and financial mathematics, molecular dynamics, and many more. PhD graduates find outstanding positions in industry and national laboratories as well as in academia.

ICME PhD students cultivate a broad and deep understanding of computational mathematics through core courses in matrix computations, optimization, stochastics, discrete mathematics, and PDEs and through their research work with ICME affiliated faculty.

For complete details, please view the Stanford Bulletin:  Doctor of Philosophy in Computational and Mathematical Engineering

ICME PhD Admissions Requirements

Academic Programs

• CSE PhD Overview
• Dept-CSE PhD Overview
• CSE Doctoral Theses
• Program Overview and Curriculum
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MIT Doctoral Programs in Computational Science and Engineering

The Center for Computational Science and Engineering (CCSE) offers two doctoral programs in computational science and engineering (CSE) – one leading to a standalone PhD degree in CSE offered entirely by CCSE (CSE PhD) and the other leading to an interdisciplinary PhD degree offered jointly with participating departments in the School of Engineering and the School of Science (Dept-CSE PhD).

While both programs enable students to specialize at the doctoral level in a computation-related field via focused coursework and a thesis, they differ in essential ways. The standalone CSE PhD program is intended for students who intend to pursue research in cross-cutting methodological aspects of computational science. The resulting doctoral degree in Computational Science and Engineering is awarded by CCSE via the the Schwarzman College of Computing. In contrast, the interdisciplinary CSE PhD program is intended for students who are interested in computation in the context of a specific engineering or science discipline. For this reason, this degree is offered jointly with participating departments across the Institute; the interdisciplinary degree is awarded in a specially crafted thesis field that recognizes the student’s specialization in computation within the chosen engineering or science discipline.

For more information about CCSE’s doctoral programs, please explore the links on the left. Information about our application and admission process is available via the ‘ Admissions ‘ tab in our menu. MIT Registrar’s Office provides graduate tuition and fee rates as set by the MIT Corporation and the Graduate Admissions section of MIT’s Office of Graduate Education (OGE) website contains additional information about costs of attendance and funding .

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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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40 PhD jobs in Computational Sciences

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• PhD positions in Computer Science (169)

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PhD Candidate for Computational Approaches for Studying Animal Behavior, Personality, and Emotions

About our Campus: Rehovot Campus (Rehovot) and Mt. Scopus Campus (Jerusalem). The position will be based across two campuses: The Robert H. Smith Faculty of Agriculture, Food and Environment on The Hebrew University’s beautiful Rehovot Campus. Reh...

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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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• Masters Degree in Computational Science

PhD student positions in Scientific Computing 2019

The Division of Scientific Computing at Uppsala University is a leading center for research and education in Scientific Computing. The research has a broad scope, ranging from numerical analysis over software development and high-performance computing to collaborative projects in Computational Science and Engineering, and industrial applications. The research is organized in two research programs (Computational Science and Numerical Analysis) with a common, rather extensive graduate (PhD) education program in Scientific Computing. Many of the PhD student projects include collaboration and joint supervision with other partners, both at Uppsala University and, e.g., Stanford University, Texas A&M University, and ETH Zürich. There is more information on the research at the Division.

The Division has an extensive international and national research network, including participation in, e.g., the Uppsala-Lund-Umeå e-Science consortium eSSENCE, which is a new major Strategic Research Area effort funded by the Swedish Ministry of Education. The Division also participates in the Uppsala Centre for Interdisciplinary Mathematics (CIM) as well as in the parallel programming Centre of Excellence UPMARC funded by the Swedish Research Council. Several research projects at the Division are also part of collaborations with Science for Life Laboratory (SciLifeLab).

The Division hereby announces three new PhD student positions. The application should include a letter with a description of the candidate's research interests and earlier experience. Links to more extensive descriptions of the PhD projects are provided below. The application should also contain a CV, copies of exams, degrees and grades, a copy of the Master thesis (or a draft thereof), relevant publications, and other relevant documents. The plan is to invite a selection of the candidates to an interview before the position is filled.

A PhD student position requires a Master of Science or an equivalent exam in a field which is relevant for the topic of the PhD thesis. However, candidates which are expected to be awarded a Master degree soon (e.g. currently working on their Master's thesis) are also eligible to apply. Candidates should mention the earliest feasible starting date for the employment.

The PhD student position is for a maximum of five years of study and includes departmental duties at a level of at most 20% (typically teaching).

For further information please contact one or more of the contact persons given in the descriptions of proposed projects below. You may also contact the Head of division Emanuel Rubensson .

PhD Student Projects

More information about the available projects can be found through the links below

• 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:

Carly Gubitz [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

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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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Computer Science (PhD)

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The doctor of philosophy in computer science is, above all, a high-quality degree that is not conferred simply for the successful completion of a specified number of courses or years of study. It is a degree reserved for students who demonstrate a comprehensive understanding of computer science and an ability to do creative research. Each PhD student produces a significant piece of original research, presented in a written dissertation and defended in an oral examination.

The PhD program is structured to facilitate the process of learning how to do research. Students begin by taking required courses to build a foundation of knowledge that is essential for advanced research. Early in the program the student gains research experience by undertaking a directed research project under the close supervision of a faculty member and the scrutiny of a faculty committee. In the later stages of the program, students take fewer courses and spend most of their time exploring their dissertation area to learn how to identify and solve open problems. The final steps are to propose an independent research project, do the research, and write and defend a dissertation.

Application materials should be submitted by December 15 for the following fall term. Materials include everything required for admission to the master’s program as well as a discussion of the anticipated research area.

Students who enter the UO with a master’s degree may petition the Graduate Education Committee for credit toward the course requirements listed below, indicating how their prior graduate work corresponds to these courses. See the graduate coordinator for the petition.

Program Learning Outcomes

Upon successful completion of this program, students will be able to:

• Core Knowledge Breadth: Demonstrate a broad working knowledge of fundamental theories, research findings and methodological approaches in multiple content areas within Computer Science (Foundations, Systems, Data Science).
• Core Knowledge Depth: Demonstrate a deep working knowledge of advanced theories, research findings, and methodological approaches within one of the Computer Science areas of Foundations, Systems, and Data Science.
• Software Engineering: Demonstrate a working knowledge of software engineering and development techniques and related hands-on skills.
• Scientific Inquiry: Achieve a deep fluency in the scientific literature and the ability to ask and pursue compelling questions within a primary field of research, and achieve proficiency in relevant experimental design, methodology, and data analysis/statistical methods.
• Scientific Communication: Demonstrate effective oral and written scientific communication skills.

PhD Course Requirements

A grade of B- or better is required

Cannot duplicate Depth course used

Cannot duplicate Breadth course used

A grade of C or better is required in graded elective credits

PhD Degree Requirements

PhD candidates who enter the program without a master’s degree in computer science must take 48 credits in graduate course work including the core and cluster courses required for the MS program. Doctoral students must earn a minimum grade of B– and an overall GPA of 3.50 in the six courses they use to satisfy the breadth and depth requirements.

Minimum Annual Enrollment

PhD students are expected to enroll in at least 6 credits of 600-level course work each year until their advancement to candidacy. Research: [Topic] (CS 601), Dissertation (CS 603), and Reading Conference: [Topic] (CS 605) do not satisfy this requirement. After candidacy, PhD students are encouraged to continue participation in 600-level courses

Directed Research Project

Complete a directed research project, which is supervised by a faculty member and evaluated by a faculty committee. The research project comprises the following:

• The definition and expected results of the project in the form of a Directed Research Project Contract
• Delivery of the materials constituting the results of the project and oral presentation of the results
• A private oral examination by the committee members

Status Change

PhD candidates are admitted conditionally. Successful completion of the directed research project leads to a change in the student’s doctoral status from conditional to unconditional.

Dissertation Advisory Committee

After successfully completing the directed research project, PhD students form a Dissertation Advisory Committee chaired by their research advisor. The main role of the committee is to advise the student between completion of the research project and mounting the dissertation defense. The committee takes primary responsibility for evaluating student progress. In addition, it approves the plan for the area examination, which in turn is approved by the graduate education committee. See the graduate coordinator for further instructions.

Area Examination

The student chooses an area of research and works closely with an advisor to learn the area in depth by surveying the current research and learning research methods, significant achievements, and how to pose and solve problems. The student gradually assumes a more independent role and prepares for the area examination, which tests depth of knowledge in the research area. The examination contains the following:

• A survey of the area in the form of a position paper and an annotated bibliography
• A public presentation of the position paper
• A private oral examination by committee members

Advancement to Candidacy

After the area examination, the committee decides whether the student is ready for independent research work; if so, the student is advanced to candidacy.

Dissertation and Defense

Identify a significant unsolved research problem and submit a written dissertation proposal to the dissertation committee. The dissertation committee, comprising three department members and one member from an outside department, is approved by the graduate education committee. In addition to these four, the dissertation committee often includes a fifth examiner. This outside examiner should be a leading researcher in the candidate’s field who is not at the University of Oregon. The outside member should be selected a year before the candidate’s dissertation defense, and no later than six months before.

The student submits a written dissertation proposal to the committee for approval, and the proposal is then submitted to the graduate education committee. The proposal presents the research problems to be tackled, related research, methodology, anticipated results, and work plan. The committee may request an oral presentation, similar to the area exam, which allows the student to explain and answer question about the proposed research. The student then carries out the research.

The final stage is writing a dissertation and defending it in a public forum by presenting the research and answering questions about the methods and results. The dissertation committee may accept the dissertation, request small changes, or require the student to make substantial changes and schedule another defense

Division of Graduate Studies Requirements

PhD students must meet the requirements set by the Division of Graduate Studies as listed in that section of this catalog

Research Areas

It is important that a PhD student be able to work effectively with at least one dissertation advisor. Hence the student should identify, at an early stage, one or more areas of research to pursue. The student should also find a faculty member with similar interests to supervise the dissertation.

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Germany: 15 Interdisciplinary PhD positions in Scientific Computing, Data Science and Physics

Data Science in Hamburg - Helmholtz Graduate School for the Structure of Matter (DASHH) is a recently established, interdisciplinary graduate school that offers challenging Ph.D. topics at the interface of the natural sciences, applied mathematics and data and computer science. DASHH involves several research institutions and universities in the multifaceted city of Hamburg, Germany.

DASHH is looking for excellent and highly qualified Ph.D. candidates,

interested to work on data-driven research in physics, chemistry, applied mathematics, computer science or structural biology.

• Interdisciplinary research in the natural sciences and computer/data science or applied mathematics

• Research at world leading large-scale research facilities (PETRA III, FLASH, European XFEL, LHC)

• Attractive, interdisciplinary thesis topics

• Excellent working conditions at an international, vibrant and inspiring research campus

• Close supervision and support by a panel of established professors and scientists

• Training in transferable skills and career development

• Work contract at the level of the German TV-L13 (100%) salary scheme for 3 years

Requirements

In order to apply you need to have a Master's Degree (or an estimated date of Graduation within the same year of application) in computer science, applied mathematics or natural sciences, preferably with an interdisciplinary training at the interface of natural and computer science or mathematics. Degrees from foreign universities and master degrees in non-research oriented study programs (Fachhochschule) might be eligible. For further information, consult the doctorate regulations (Promotionsordnung) of the respective partner university.

All prospective students must submit proof of adequate English proficiency.

Before you start to prepare your application we strongly encourage you to read the admission requirements that also list the necessary documents you will need for your application. You can apply for up to two PhD research topics of your interest.

The following documents are required for your application:

• Letter of Application (Why are you interested in this study program? What are your current research interests? In English, maximum 2 pages)

• Curriculum Vitae with relevant credentials, qualifications and list of publications (in English, maximum 2 pages).

• Certificates of your Master and Bachelor Degrees (We accept degrees in German or English language, or certified and notarised translations of your degree)

• Transcript of Records (in German or English, or certified and notarised translations thereof)

• Abstract of your Bachelor and Master thesis (in German or English)

• Proof of adequate English proficiency (see above, can be filed subsequent to the application until end of December, 2019)

• Summary of computer-science skills (Please comment on your experience with scientific software, in programming and in software development. Maximum 1 page)

• Letter of recommendation from your official Master thesis advisor (to be uploaded by you) and optionally a second letter of recommendation (by one of your professors, or previous employers or supervisors)

• Contact Information of your Master thesis advisor and a second reference, that you authorise to being contacted by our selection committee to investigate your past scientific and professional activities and academic performance by submitting your application.

Application form

Applications are accepted only via the online application portal. Please notice that email applications will not be considered.

Please carefully read our admission requirements before submitting your application. Once you have submitted your application, you will receive a confirmation of receipt of your application to your email address. We strongly recommend to keep this information for future inquiries regarding your application.

The deadline to submit your application in response to the present call is December 1, 2019.

Applicants will be chosen based on their academic qualification, motivation, scientific interest, letters of recommendation and aptitude.

Application details

PhD student in Scientific Computing

Uppsala university, application deadline, march 30, 2022.

Uppsala University, Department of Information Technology

Uppsala University is a comprehensive research-intensive university with a strong international standing. Our ultimate goal is to conduct education and research of the highest quality and relevance to make a long-term difference in society. Our most important assets are all the individuals whose curiosity and dedication make Uppsala University one of Sweden’s most exciting workplaces. Uppsala University has over 54,000 students, more than 7,500 employees and a turnover of around SEK 8 billion.

1 PhD student in Scientific Computing focusing on Statistical Sampling/Active Learning for Large-Scale Scientific Experiments

The Department of Information Technology has a leading position in research and all levels of higher education. Today the department has 280 employees, including 120 academic staff and 110 full-time PhD students. The Department comprises research and education in a spectrum of areas within Computer Science, Information Technology and Scientific Computing. More than 4000 students take one or several courses offered by the Department each year.

The position is hosted by the Division of Scientific Computing within the Department of Information Technology. As one of the world’s largest focused research environments in Scientific Computing the research and education has a unique breadth, with large activities in classical scientific computing areas such as mathematical modeling, development and analysis of algorithms, scientific software development and high-performance computing. The division is currently in an expansive phase in new emerging areas such as cloud and fog computing, data science, and artificial intelligence, where it plays key roles in several new strategic initiatives at the University. The division currently hosts 20 PhD students, with more than 80 doctorates awarded. Several PhD alumni from the division are successful practitioners in the field of scientific computing and related areas, in industry as well as in academia.

The position is also part of the Science for Life Laboratory (SciLifeLab) network and offers a rich and highly interdisciplinary research environment. SciLifeLab is a leading institution and national research infrastructure with a mandate to enable cutting-edge life sciences research in Sweden, foster international collaborations, and attract and retain knowledge and talent. Our research group specializes in developing theory, methods and software for intelligent scientific experiments. We have a wide network of collaborators, and there will be opportunities to work together with excellent researchers within Sweden and abroad.

Project description The closely related fields of statistical sampling, active learning and sequential design of experiments (DoE) aim to maximize information gain with a specified objective. Sampling in this context is an iterative process wherein a small number of well-chosen candidate samples are generated and evaluated for a task (e.g., evaluation of drug combinations for treating diseased cells). The goal of the sampling process is to identify the (pseudo) optimal samples for the given task (or the samples that meet a pre-defined criterion, e.g., specific efficacy rate of treating diseased cells). 

Techniques such as Bayesian and surrogate-based optimization and surrogate-driven adaptive sampling exist as data-efficient approaches in this space. However, large-scale problems that may involve several millions of candidate samples (and/or several variables/attributes per sample (>50)) still suffer from the curse of dimensionality.

This project aims to address sequential sampling in high dimensions for large-scale problems. Specifically, we will focus on scientific experiments such as large-scale parameter sweeps and optimal drug combination selection as applications. Some questions to study and explore include divide and conquer approaches in high-dimensional search spaces, accelerating Bayesian model-based sampling for large candidate sets and deriving sampling uncertainty estimates in high dimensions.

Duties The duties of a PhD student are primarily directed at their own research education, which lasts four years. The work may also involve, to a limited extent (ca 20%) other departmental duties, such as teaching undergraduate courses and administrative tasks – in which case the position may be extended to a maximum of five years.

Requirements A PhD position at the Division requires a Master of Science or equivalent in a field that is relevant to the topic of the PhD thesis, good communication skills and excellent study results, as well as sufficient proficiency in oral and written English. Additional requirements for this position include basic knowledge of, and interest in machine learning, and proficiency in programming (e.g., in Python/Julia). Extra merits with equal weights include knowledge and experience in numerical optimization, Bayesian methods and deep learning, bioinformatics and/or computational biology, and best practices in software engineering.

Application Your application should include:

• a personal letter describing your background, relevant qualification, research interests and how you see that your background would contribute to the specific project.
• Curriculum Vitae,
• degree documents (for any completed degree) and transcript of records (including completed courses and grades) to support that you’re qualified for admission to education at the PhD level,
• list of publications (if any),
• copy of your master thesis, or a detailed description of the ongoing thesis work, including a plan for its completion, if not fully complete at the end of the application period,
• list of reference persons with contact details,
• any other supporting documentation demonstrating your suitability for the position.

It is not a strict requirement for the relevant pre-requisite degree to be completed when the application is made. All applicants should state when they would be able to fulfill all requirements and be ready to assume the position.

Last updated: 2022-03-02

Content Responsible: David Gotthold( [email protected] )

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Scientific computing career advice

Undergrad here; I'd love some advice about this potential career path.

I'm currently an undergrad at Georgia Tech, majoring in mechanical engineering and minoring in scientific computing. I plan to do my school's online master's in CS after I graduate.

I didn't major in CS because I don't want to get shoehorned into a CS job I don't find interesting; my CS interests are relatively narrow. I love high-performance computing, algorithms, numerical methods, and simulation. I have no interest in web-dev, servers, apps, databases, etc. My back-up (which I'd still enjoy) is to be a mechanical engineer.

I'm a scientist at heart though. I love solving scientific problems and creating new knowledge. If the field wasn't so oversaturated and paid so poorly, I'd probably get a PhD and go into academia. But I'd rather not spend five years making nearly nothing doing a PhD and five more years making $40k as a post-doc for the off-chance of getting a tenure-track position.

I think I've found a good career option that combines science, engineering, and CS. Scientific software sounds challenging and interesting. Here are some jobs I think I'd like: 1 , 2 , 3 , 4 .

How should I pursue this? I have an internship this summer at NIST engineering lab; I'll be doing data analysis and simulation for their energy research. My ME degree already gives me a base of knowledge in physics, math, thermodynamics, fluid mechanics, and other foundation science.

What should I do to develop CS knowledge? It seems like NumPy, R, Matlab, Tensorflow, and other scientific tools would be useful. I'm reasonably proficient at Java and Matlab and next semester I'll be learning C.