223 parallel-and-distributed-computing-"Meta"-"Meta" positions at NIST in United States
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RAP opportunity at National Institute of Standards and Technology NIST Integrating Data and Computational Tools for Advanced Materials Design Location Material Measurement Laboratory, Materials
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RAP opportunity at National Institute of Standards and Technology NIST High-performance Computing and Data Visualization for the NIST Fire Dynamics Simulator Location Engineering Laboratory
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Computational Methods for NMR Structural Biology and Biomanufacturing of Protein and Live Cell Therapeutics NIST only participates in the February and August reviews. Protein therapeutics
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the development towards this end of efficient, highly parallel software running on commodity hardware. Novel methods to compute the stray field from magnetized material with attention to interface and
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301.975.8582 Michael Garth Huber michael.huber@nist.gov 301 975 5641 Description This program explores complementary aspects of atom and neutron interferometry with particular emphasis on their interplay with
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scaled up to handle large numbers of samples in massively parallel, low-cost analysis systems. Before such systems can be realized, the electromagnetic response of biochemical samples must be understood in
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chromatography, hydrophobic precipitation and tangential flow filtration, etc. are also utilized [3]. Current approaches for characterizing the particle size distribution and/or particle number concentration
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also have familiarity with X-ray Diffraction and Pair Distribution Function anaylisis, experience with data acquisition using the Bluesky experiment orchestration package, and knowledge of beamline
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is to measure to high accuracy the SI-traceable spectral energy distribution over the visible and near infrared wavelength range for a set of stars for use as flux standards for astronomy. In
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, robotics), cyberinfrastructure (e.g., databases, high-performance computing, collaboration tools), and humans (e.g., scientists, engineers, students, managers). The recent interest in Explainable AI (XAI