141 computational-material-science-"Multiple" Postdoctoral positions at Princeton University
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experimental research related to multiple ongoing projects, including optical diagnostic design and high-temperature ammonia oxidation chemistry with applications to green manufacturing and recycling of steel
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Postdoctoral research positions in Astrophysical Sciences The Department of Astrophysical Sciences, Princeton University, anticipates offering a number of postdoctoral or more senior research
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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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efforts. Training also includes an introduction to various advanced neuroimaging methodologies. Essential qualifications for these positions include: a Ph.D. in Neuroscience, Computer Science
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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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, working under the guidance of Dr. Arash Adel, Assistant Professor in the School of Architecture and Associated Faculty of the Department of Computer Science. The desired start date is Spring 2025
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the guidance of Dr. Arash Adel, Assistant Professor in the School of Architecture and Associated Faculty of the Department of Computer Science. The desired start date is Spring 2025. Appointments are for one
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on social vulnerability to hazards. The researcher will have the opportunity to work on multiple projects, investigating (a) cumulative environmental impacts, (b) the use of census microdata for social
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: 277494300 Position: Postdoctoral Research Associate in Microfluidics, Nanofabrication, and Nanophotonics Description: The Department of Electrical and Computer Engineering has opening for postdoctoral
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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