153 proof-checking-postdoc-computer-science-logic Postdoctoral positions at Princeton University in United States
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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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computational chemistry. The Term of appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility of renewal pending satisfactory performance and continued funding; those
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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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, 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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., 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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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
The Atmospheric and Oceanic Sciences Program at Princeton University, in association with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks a postdoctoral or more senior research scientist
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. The DataX Postdoctoral Research Associate positions are intended for early-career scientists with a research interest in data science, statistics, and machine learning. As an associate, you will join 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