Sort by
Refine Your Search
-
, single molecule biophysics, biomaterials, materials chemistry, fluid mechanics, rheology, and computational modeling. Candidates should apply at https://puwebp.princeton.edu/AcadHire/position/38901 and
-
, lipid vesicles, polymer physics, active materials, single molecule biophysics, biomaterials, materials chemistry, fluid mechanics, rheology, and computational modeling. Candidates should apply at https
-
communication skills Expertise in Generative AI: Strong background in machine learning, with specific experience in Large Language Models (LLMs), and Vision-Language Models (VLMs) Excellent programming skills
-
The Department of Molecular Biology at Princeton University currently has research positions available at the postdoctoral and more senior research levels in the areas of biochemistry, biophysics
-
on data science and engineering. The scientist will collaborate with Princeton and GFDL researchers to enhance, analyze and deliver high-resolution earth system model data, with an emphasis on Seamless
-
to ion beams with well-controlled energies and incident angles for benchmarking and validation of theoretical calculations and computational physics and chemistry modeling of important surface processes
-
. Applicants should apply at: https://puwebp.princeton.edu/AcadHire/position/39183 and include: *A cover letter *A CV (including a list of publications) *A statement of relevant past and current research
-
research group of a current CSML participating faculty member and collaborate with the larger data science community. You will have the opportunity to engage with other researchers in a collaborative
-
attention and decision making networks in a behaving animal model together with parallel studies in humans. The project is part of a NIMH Silvio O. Conte Center on the "Cognitive Thalamus". The successful
-
computational modeling techniques to study planning in rodents engaged in dynamic spatial foraging tasks. The successful candidate will develop computational models of reinforcement learning in the brain and