Sort by
Refine Your Search
-
Listed
-
Field
-
well as opportunities for collaboration with Princeton faculty and graduate students. Postdoctoral Research Associates may participate in the teaching program if mutually agreed, with sufficient course enrollments, and
-
at Princeton University.We welcome applications from all areas in mechanical and aerospace engineering, including but not limited to the fields of: Bioengineering Combustion and Energy Science Computational
-
computer science, economics, law, political science, philosophy, and related fields. The DeCenter is a newly established interdisciplinary hub at Princeton University devoted to exploring the decentralization
-
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
-
modeling, or agent-based modeling, who are eager to incorporate socio-political dynamics into their models; or2.Computational social scientists with experience in empirical research and/or theoretical
-
. The University also offers a comprehensive benefit program to eligible employees. Please see this link for more information. Requisition No: D-26-MOL-00002 PI278656789 Create a Job Match for Similar Jobs About
-
for peer reviewed publications Qualifications*Ph.D. in Environmental/Civil Engineering, Computer Science/Engineering, Data Science, or a closely related field*Proficiency in Python or other tools and ML
-
: 278255392 Position: Postdoctoral Research Associate in Microfluidics, Nanofabrication, and Nanophotonics Description: The Department of Electrical and Computer Engineering has opening for postdoctoral
-
, molecular biology, biochemistry, physics, computer science, and genetics. The term of appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility of renewal pending
-
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