144 computer-science-image-processing Postdoctoral research jobs at Princeton University
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development, service delivery with an overarching objective connecting science with society. This encompasses the development of infrastructure and processes for data standardization, data warehousing, quality
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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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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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, 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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: 277494260 Position: Postdoctoral Research Associate Description: The Computer Science Department invites applications for postdoctoral and more senior research positions. Individuals with evidence of
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engineering, or in a relevant engineering field, with an extensive background and training in the operation of a wide range of spectroscopic and imaging techniques for materials characterization. Use of HRTEM
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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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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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. 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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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