Princeton University: Office of the Dean of the Faculty: Natural Sciences: Geosciences
Location: Princeton, NJ
Salary Range or Pay Grade: Postdoctoral Research Associate: $65,000 - $73,000; Associate Research Scholar: $66,000 - $78,000
Description
The Department of Geosciences at Princeton University invites applications for a researcher at the Postdoctoral Research Associate or more senior position to work on a newly funded interdisciplinary research project supported by the Princeton AI Lab Seed Grant Program in the research group of Professor Jie Deng.
This position is part of a collaborative project involving close interaction with faculty in Computer Science at Princeton University, with the goal of developing next-generation machine-learning–based interatomic potentials (MLPs) for Earth and planetary materials across extreme pressure–temperature conditions. The project sits at the interface of computer science, computational materials science, and Earth and planetary sciences, and emphasizes cross-disciplinary collaboration.
The term of appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility of renewal pending satisfactory performance and continued funding; those hired at more senior ranks may have multi-year appointments. Salary and benefits are competitive and commensurate with experience, following Princeton University guidelines.
Research Scope
- Benchmarking and evaluating existing foundation models and machine-learning potentials for planetary materials.
- Curating and generating large-scale ab initio datasets across wide pressure–temperature regimes.
- Designing and training advanced machine-learning models (e.g., graph neural networks, equivariant architectures) in collaboration with computer science researchers.
- Applying developed models to problems in Earth and planetary interiors, such as melts, phase transitions, and transport properties.
This position is subject to the University's background check policy.
The work location for this position is in-person on campus at Princeton University.
Qualifications
Required Qualifications
- PhD in Computer Science, Materials Science, Physics, Chemistry, Geosciences, or a related field.
- Strong background in machine learning, scientific computing, or computational materials science.
- Ability and interest to work in a highly collaborative, interdisciplinary research environment.
Preferred Qualifications
- Experience with graph neural networks, equivariant models, or foundation models.
- Familiarity with atomistic simulations (e.g., density functional theory, molecular dynamics).
- Interest in developing broadly applicable machine-learning methods for physical sciences.
Application Instructions
Applicants should submit (1) a cover letter describing research interests and relevant experience; (2) a curriculum vitae including a publication list; and (3) contact information for three references.
Inquiries about the position may be sent to jie.deng@princeton.edu with the subject line “Deng Postdoc Inquiry 2026”. Applications will be reviewed on a rolling basis until the position is filled.
Equal Employment Opportunity Statement
Princeton University is an Equal Opportunity Employer and all qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status, or any other characteristic protected by law.
Pay Transparency Disclosure
The University considers factors such as (but not limited to) the scope and responsibilities of the position, candidate's qualifications, work experience, education/training, key skills, market, collective bargaining agreements as applicable, and organizational considerations when extending an offer. The posted salary range represents the University's good faith and reasonable estimate for a full-time position; salaries for part-time positions are pro-rated accordingly.
The University also offers a comprehensive benefits program to eligible employees. Please see this link for more information.
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