31 high-performance-computing Postdoctoral positions at Princeton University in United States
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package development and maintenance in R; record linkage/entity resolution; data privacy techniques; large data processing and high performance computing; advanced causal inference and statistics; computer
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technologies. The Pritykin lab (http://pritykinlab.princeton.edu ) develops computational methods for design and analysis of high-throughput functional genomic assays and perturbations, with a focus on multi
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. Applicants with experience in the following areas are encouraged to apply: Experimental condensed matter physics, cosmology and astrophysics, particle astrophysics and dark matter, high energy, atomic, pulsar
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CONTEXT A fully accredited facility, University Health Services (UHS) at Princeton University provides responsive, high quality clinical, preventive, and consultative health services to over 8,000
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are encouraged to apply: Experimental condensed matter physics, cosmology and astrophysics, particle astrophysics and dark matter, high energy, atomic, pulsar and biophysics; theoretical cosmology, condensed
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, high quality clinical, preventive, and consultative health services to over 8,000 Princeton undergraduate and graduate students and their dependents, and occupational health services to Princeton
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The postdoctoral track of the CITP fellows' program is for people who have recently received or are about to receive a Ph.D. or doctorate degree and work on understanding and improving
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University invites applications for postdoctoral positions. Our lab works in the areas of ultrafast science, nanoscale thermal transport, and microelectronics, for applications in energy-efficient computing
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(energy auditing, GHG accounting, resource recovery)*Strong publication record and excellent written/verbal communication skills*Experience in coding for high performance computing (e.g., university cluster
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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