Sort by
Refine Your Search
-
Process - All candidates must use the online application process to submit materials at: https://puwebp.princeton.edu/AcadHire/position/39861 Applicants are required to hold a Ph.D. or expect to receive a
-
competition for the 2026-2027 Harry Hess Fellows Program. This honorific postdoctoral fellowship program provides opportunities for outstanding geoscientists to work in the field of their choice. Research may
-
experience in scholarly research and a strong commitment to excellence in education are encouraged to apply. A PhD in Materials Science, Optics, Physics, Chemistry, Electrical, Chemical, Mechanical, Civil
-
, lipid vesicles, polymer physics, active materials, single molecule biophysics, biomaterials, materials chemistry, fluid mechanics, rheology, and computational modeling. Candidates should apply at https
-
Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
: 277494287 Position: Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning Description: The Atmospheric and Oceanic Sciences Program at Princeton University, in
-
University invites applications for postdoctoral positions. Our lab works in the areas of ultrafast science, nanoscale thermal transport, and microelectronics, for applications in energy-efficient computing
-
interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
-
Library and to a wide range of activities throughout the University. This position is subject to the University's background check policy. Appointments are for one year. Applicants cannot be in the process
-
: 275347872 Position: Postdoctoral Research Associate Description: Postdoctoral and more senior research positions are available in biological, inorganic, materials, organic, physical, theoretical, and
-
interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials