Sort by
Refine Your Search
-
Listed
-
Field
-
behavioral paradigms and combined with computational approaches. We are seeking an extremely motivated postdoctoral researcher with background in human or monkey electrophysiology. Studies will include
-
are for one year with the possibility of renewal pending satisfactory performance and continued funding; those hired at more senior ranks may have multi-year appointments. Please include a cover letter, CV and
-
strong commitment to excellence in education are encouraged to apply. A Ph.D. is required. Postdoctoral appointments are for one year with the possibility of renewal pending satisfactory performance and
-
, with a possibility for renewal contingent upon satisfactory performance and available funding. Applicants must have a Ph.D. in Geosciences or a closely-related field. Applicants should include a cover
-
The Department of Computer Science at Princeton University is seeking applications for postdoctoral or more senior research positions in theoretical computer science and theoretical machine learning
-
Fellows Program. The Program recognizes and supports outstanding early-career scientists who can make important research contributions in the areas of ecology, evolution, and/or behavior, while also
-
performance and continued funding; those hired at more senior ranks may have multi-year appointments. These positions are subject to the University's background check policy. The work location for this position
-
of renewal pending satisfactory performance and continued funding; those hired at more senior ranks may have multi-year appointments. Applicants must apply online at https://puwebp.princeton.edu/AcadHire
-
fields. Appointments are typically for one year with the possibility of extensions up to three years pending satisfactory performance and continued funding and the work location for this position is in
-
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