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Plasma Physics Lab and in the Physics, Geosciences, and Mechanical and Aerospace Engineering Departments, and at the nearby Institute for Advanced Study. The expected start date is September 1, 2026
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and society, is developing an emerging research and teaching program in design that embraces Princeton's commitment to the betterment of humanity through deliberative, informed, and thoughtful design
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biology, cancer biology, chemical biology, biochemistry, cancer genomics, genetics, mass spectrometry, physical chemistry, computational and systems analysis. The term of appointment is based on rank
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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
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: 277494288 Position: Postdoctoral Research Associate - Moore Foundation Description: Through the department of Physics at Princeton University, Openings are available for a postdoctoral research associate as
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, lipid vesicles, polymer physics, active materials, single molecule biophysics, biomaterials, materials chemistry, fluid mechanics, rheology, and computational modeling. Candidates should apply at https
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experience in scholarly research and a strong commitment to excellence in education are encouraged to apply. A PhD in Materials Science, Optics, Physics, Chemistry, Electrical, Chemical, Mechanical, Civil
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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
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
: 277494287 Position: Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning Description: The Atmospheric and Oceanic Sciences Program at Princeton University, in
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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