141 proof-checking-postdoc-computer-science-logic Postdoctoral positions at Princeton University in United States
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of squamate reptiles; the largest group 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
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Department of Chemical and Biological Engineering at Princeton University. The position is in the broad area of renewable energy systems synthesis, analysis, and optimization. The goal of the project is to
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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 with a variety of programming languages, and familiarity with critical path planning tools, are essential. A Ph.D. in engineering, operations research, computer science, or another related field is
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retrotransposition using an integrated biochemical and structural approach with a focus on cryo-EM. The postdoctoral scholar will have access to cutting-edge cryo-EM instrumentation and computational resources through
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., geography, urban planning, data science, sociology, public health, emergency management). Ideal applicants will have: *Expertise conducting spatial and statistical analyses *Experience with scientific
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approach with a focus on cryo-EM. The postdoctoral scholar will have access to cutting-edge cryo-EM instrumentation and computational resources through the various core facilities at Princeton University
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; biodiversity; conservation; environmental science and policy; infectious disease and global health; and sustainable development in impoverished and resource-challenged regions of the world. The Term
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. Essential qualifications for this position include: a Ph.D. in Neuroscience, Psychology, Cognitive Science, Computer Science, Engineering, or other related field, and strong experience with computational
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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