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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 11 hours ago
University to support outstanding research. We will foster programs in the areas of basic, translational, mechanistic, and population research. Position Summary The Phanstiel lab at UNC (http://phanstiel
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develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel solver, capable of running
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may close at any time) Job Summary The Ho lab at the University of Utah is launching an ambitious program to systematically discover, map, and therapeutically harness mitochondrial microproteins
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collaborating experimental research groups. Previous experience in computational modeling of atmospheric aerosols and parallel computing/software development is strongly desired. The term of appointment is based
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Computing Applications group in CSD has an immediate opening for a Postdoctoral Research Associate to design, develop, and deploy machine-learning and high-performance computing workflows, algorithms, and
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parallel computing. Demonstrated hands-on experience and understanding of developing scientific data management, workflows and resource management problems. Strong problem-solving and communication skills
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Oak Ridge National Laboratory, Mathematics in Computation Section Position ID: ORNL-POSTDOCTORALRESEARCHASSOCIATE5 [#27233] Position Title: Position Location: Oak Ridge, Tennessee 37831
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Oak Ridge National Laboratory, Mathematics in Computation Section Position ID: ORNL-POSTDOCTORALRESEARCHASSOCIATE3 [#27208] Position Title: Position Type: Postdoctoral Position Location: Oak Ridge
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. Experience in developing and applying advanced parametric/machine learning postprocessing techniques, producing probabilistic forecasts of hydrometeorological variables, and parallel computing. Proficiency in
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, Climate Science or a related field. Experience in earth system modeling, data assimilation, and remote sensing of land surface variables. Experience with parallel computing on high performance cluster