Postdoctoral Scholar

Postdoctoral Scholar in Machine Learning for Physical Systems

The Department of Electrical and Computer Engineering at Tufts University invites applications for a Postdoctoral Scholar in the research group of Prof.

Peter Lu.

The position focuses on machine learning methods for physical systems, including ML emulators or surrogate models for chaotic dynamics and materials modeling.

The successful candidate will develop and analyze ML models for scientific problems and will have latitude to shape the research direction in collaboration with the PI

Relevant application domains include high-dimensional PDEs, turbulence, and materials modeling.

Methodological interests in the group span scientific generative modeling, representation learning, optimal transport, and neural operators.

Candidates whose expertise connects to any of these areas, and who want to work at the interface of rigorous physical modeling and modern ML are encouraged to apply.

include conducting independent and collaborative research, developing and validating scientific ML models and code, disseminating results through publications and presentations, and contributing to grant activity and the mentoring of student researchers.

a PhD (completed or expected before the start date) in electrical and computer engineering, physics, applied mathematics, computer science, or a closely related field; a strong record of research; and proficiency in scientific computing and modern ML frameworks.

demonstrated experience in one or more of PDEs, dynamical systems, turbulence, materials modeling, or ML surrogate models; familiarity with differentiable programming; and a background spanning both physical modeling and machine learning.

The preferred start date is as soon as possible.

The appointment is for up to two years: an initial one-year term with a second year contingent on performance and funding.

follows the Tufts compensation schedule for postdoctoral scholars and is commensurate with experience.

To apply, please submit a CV, a brief statement of research interests, and two confidential letters of reference.

Review of applications will begin immediately and continue until the position is filled.

Inquiries may be directed to Prof.

Peter Lu ([email protected]).