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of Engineering and Applied Science invites applications for postdoctoral and more senior research positions. Individuals with evidence of experience in scholarly research and a strong commitment to excellence in
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should have a PhD degree (or expect to receive a PhD degree by June 15, 2026) in Psychology with demonstrated expertise in gender development. Applicants should have expertise in video coding (e.g
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the world's most pressing challenges. The PIIRS Postdoctoral Fellows Program is integral to that mission. We will award up to four postdoctoral fellowships to our 2027-28 cohort. PIIRS seeks recent PhDs in
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://loogroup.princeton.edu/) is searching for a postdoctoral researcher interested in advancing the stability of perovskite solar cells. Applicants must have (or expect to have at time of appointment) a PhD in chemical
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to that mission. We will award up to four postdoctoral fellowships to our 2027-28 cohort. PIIRS seeks recent PhDs in the Social Sciences who have demonstrated exceptional scholarship, congruent with the Institute's
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. Qualifications Required Qualifications PhD in Computer Science, Materials Science, Physics, Chemistry, Geosciences, or a related field. Strong background in machine learning, scientific computing, or computational
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and collaborative research on the above topics. Preference will be given to candidates that have obtained their PhD within the last year. While this listing is open to all social science disciplines
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at Princeton University currently has research positions available at the postdoctoral or more senior research levels in the areas of neuroscience, psychology, molecular biology, biochemistry, physics
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required. Intermediate knowledge in C/C++ and/or at least one SQL dialect is preferred. Apply online at https://www.princeton.edu/acad-positions/position/40801. Review of applications will begin on December
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Department invites applications for postdoctoral and more senior research positions in two areas of computer science. 1) Quantum computing and quantum architecture and systems. 2) Reinforcement learning