133 formal-verification-computer-science Postdoctoral research jobs at Princeton University
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modeling. Positions may begin as soon as September 2025 . A Ph.D. in Mechanical and Aerospace Engineering, Civil and Environmental Engineering, Atmospheric and Oceanic Sciences, Geosciences, Computational
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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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, 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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Engineering, Computer Science/Engineering, Data Science, or a closely related field *Proficiency in Python or other tools and ML frameworks *Track record of open source contributions or tool development in AI
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Geochemistry, Geomicrobiology, Environmental Chemistry, Biogeochemical Cycles, Paleoclimatology, Oceanography, Atmospheric Science, Geodynamics, Geochronology, Earth History, Seismology, and Planetary Science
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Department of Geosciences PRINCETON UNIVERSITY HARRY HESS FELLOWS PROGRAM The Department of Geosciences at Princeton University announces competition for the 2026-2027 Harry Hess Fellows Program
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. Training also includes an introduction to various advanced neuroimaging methodologies. Essential qualifications for these positions include: a Ph.D. in Neuroscience, Computer Science, Bioengineering
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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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A Postdoctoral Research Associate or more senior research position in computational biology is available in the Pritykin lab at the Lewis-Sigler Institute for Integrative Genomics and the
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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