28 high-performance-computing Postdoctoral positions at Princeton University in United States
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postdoctoral research associate position, to start as early as September 2025. The Ferris group studies high-temperature reaction chemistry and particulate formation using optical diagnostic methods, with
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scales. Specifically, the researcher will join a team of energy modelers at the Andlinger Center and partner institutions to couple a global integrated assessment model with a high-resolution subnational
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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
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: 277494302 Position: Postdoctoral Research Associate Theoretical High-Energy Physics Description: "Post-doctoral Associate in Theoretical High-Energy Physics'The Physics Department at Princeton University
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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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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 and biophysics
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publication record and excellent written/verbal communication skills *Experience in coding for high performance computing (e.g., university cluster or similar systems) is desired The term of appointment is
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://pritykinlab.princeton.edu) develops computational methods for design and analysis of high-throughput functional genomic assays and perturbations, with a focus on multi-modal single-cell, spatial and genome editing
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
: 277494287 Position: Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning Description: The Atmospheric and Oceanic Sciences Program at Princeton University, in
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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