154 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Princeton University
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of terrestrial vertebrates on Earth today with 11,000 species. A Ph.D. in Evolutionary Biology, Computational Biology, or related fields, is required. The work will focus on i) phylogenomic inference of hundreds
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their potential to explore, discover and understand emergent behavior of complex quantum matter. The Moore Postdoctoral Scholars in Theory of Quantum Materials program is an integral part of EPiQS and supports
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to conduct numerical studies for non-local flow-driven magnetized plasma instabilities and their associated nonlinear transport in astrophysical and laboratory plasmas. The applicant is required to have a PhD
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comprising immunology, cell biology, developmental biology and computational biology to complement our primary tools from intravital microscopy. The postdoctoral scholar will have direct access to a brand new
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incident angles for benchmarking and validation of theoretical calculations and computational physics and chemistry modeling of important surface processes occurring at plasma-material interfaces in fusion
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, genetics, mass spectrometry, physical chemistry, computational and systems analysis. The term of appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility
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, single molecule biophysics, biomaterials, materials chemistry, fluid mechanics, rheology, and computational modeling. Candidates should apply at https://puwebp.princeton.edu/AcadHire/position/38901 and
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Postdoctoral and more senior research positions are available in biological, inorganic, materials, organic, physical, theoretical, and computational chemistry. The Term of appointment is based
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Princeton University, Program in Applied and Computational Mathematics Position ID: 639 -PDRA1 [#24843, PACM2025] Position Title: Position Type: Postdoctoral Position Location: Princeton, New
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fields. Candidate must have excellent computational and bioinformatic skills; abilities for developing simulation models will be highly valued; experience with ancient DNA genomic datasets is encouraged