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searching for a computational postdoctoral research associate. The project is associated with atomistic plasma-surface interaction simulations, employing machine learned interatomic potentials (MLIPs
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for Energy, Environment and Sustainability *Optical Materials and Light Matter Interactions *Quantum Materials Science Qualifications A PhD in Materials Science, Optics, Physics, Chemistry, Electrical
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engineering, materials science, electrical engineering, or a related field. Have expert proficiency in the fabrication and testing of solution-processed electronic/optoelectronic devices Be familiar with
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and collaborative research on the above topics. Preference will be given to candidates that have obtained their PhD within the last year. While this listing is open to all social science disciplines
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precision measurement and an interest in studying living systems. The Leifer Lab studies how a biological neural network processes information, performs computation and generates actions. The lab is located
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quantitative and computational social science, addressing a diverse array of new data and analytic challenges, facilitating impactful multidisciplinary collaboration, scholarly advancement, and the creation
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on the scientific, technical, policy, and human dimensions of environmental issues. These areas include issues surrounding global change; energy and climate; biogeochemical cycles; molecular geochemistry
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Computer Science Department at Princeton University. We seek candidates with computational biology, bioinformatics, computer science, machine learning, statistics, data science, applied math and/or other
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science, electrical and computer engineering, sociology, public policy, information science, communication, economics, political science, psychology, philosophy, and related technology disciplines. Selected candidates
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computer science, cognitive science, or a related field is required. Applicants must apply online at https://puwebp.princeton.edu/AcadHire/position/40461 and include a cover letter, curriculum vitae, research