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Field
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strong track record of scientific publications. Familiarity with high-performance computing environments is a plus. Baylor College of Medicine is an Equal Opportunity/Affirmative Action/Equal Access
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diseases. The Post Doctoral Associate will lead projects exploring how environmental exposures and respiratory pathogens alter airway function, contribute to high-impact publications, and support grant
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between neuromodulation and fMRI. The postdoc will work on the network level perturbation of neurocircuits using high-definition neuromodulation. This postdoc will lead scientific discovery in developing
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reverse phase separations Perform high-throughput synthetic chemistry, when appropriate Work in a modern synthetic chemistry lab with advanced separations capabilities The successful candidate will be
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 3 months ago
foundation models or one of the NASA Earth Science focus areas, and be familiar with the other. The ideal candidate will also have experience with high performance computing, including NASA's High End
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using R for statistical analysis and in statistical modeling. Sound working knowledge of unix shell, high performance compute clusters and git. Willingness to learn and use a scripting language (e.g
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: Experimental condensed matter physics, cosmology and astrophysics, particle astrophysics and dark matter, high energy, atomic, pulsar and biophysics; theoretical cosmology, condensed matter, mathematical, and
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: 100% Position duration: Two years with the possibility of extension based on performance and availability of funding. Application Window Open date: April 22, 2025 Most recent review date: Friday, May 23
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to the performance of high-power accelerators such as the Spallation Neutron Source (SNS). The goal of this work is to demonstrate predictive modeling of beam distributions in a realistic accelerator, and as such
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to the performance of high-power accelerators such as the Spallation Neutron Source (SNS). The goal of this work is to demonstrate predictive modeling of beam distributions in a realistic accelerator, and as such