143 postdoc-computer-science-logic-"DIFFER" Postdoctoral positions at Princeton University
Sort by
Refine Your Search
-
Geochemistry, Geomicrobiology, Environmental Chemistry, Biogeochemical Cycles, Paleoclimatology, Oceanography, Atmospheric Science, Geodynamics, Geochronology, Earth History, Seismology, and Planetary Science
-
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
-
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
-
, computational fluid dynamics and material science, dynamical systems, numerical analysis, stochastic problems and stochastic analysis, graph theory and applications, mathematical biology, financial mathematics
-
assurance development and data analysis. A successful candidate will work closely in different aspects of the quality assurance and data engineering pipeline, developing usable and innovative solutions
-
The Form Finding Lab in the Department of Civil and Environmental Engineering (CEE) at Princeton University invites applications for a post-doctoral or more senior research position to support
-
. The University also offers a comprehensive benefit program to eligible employees. Please see this link for more information. Requisition No: D-26-SPI-00006 PI277393696 Create a Job Match for Similar Jobs About
-
: 276110766 Position: Postdoctoral Research Associate Description: A Postdoctoral Research Associate or more senior research position in computational biology is available in the Pritykin lab at the Lewis
-
benefit program to eligible employees. Please see this link for more information. Requisition No: D-26-PHI-00001 PI277293984 Create a Job Match for Similar Jobs About Princeton University Princeton
-
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