130 formal-verification-computer-science Postdoctoral research jobs at Princeton University
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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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, 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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for part-time positions are pro-rated accordingly. The University also offers a comprehensive benefit program to eligible employees. Please see this link for more information.
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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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other subfields in political science, related disciplines, or in interdisciplinary areas. While most political theorists are trained in departments of politics and political science, we welcome
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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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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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: 274438209 Position: Postdoctoral Research Associate Description: The Ferris Research Group in the Mechanical and Aerospace Engineering Department at Princeton University invites applications for a
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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