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for accelerated science. This research opportunity focuses on developing, evaluating, and applying computational methods for materials characterization and/or simulation that combine the best aspects of physics
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; Computational microscopy; Dimensional metrology; Optical imaging;
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Computational Mathematics Division opportunity location 50.77.11.B7430 Gaithersburg, MD NIST only participates in the February and August reviews. Advisers name email phone Vladimir V Marbukh marbukh@nist.gov
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RAP opportunity at National Institute of Standards and Technology NIST Applications of Machine Learning/AI to Neutron Scattering Location NIST Center for Neutron Research opportunity location 50.61.01.C0300 Gaithersburg, MD NIST only participates in the February and August...
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research program focuses on engineering these nanoparticles with desired physical and chemical properties and specified functionality through wet-chemistry synthesis. We are particularly interested in
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memory, batteries, catalysts, flexible devices, alternate computing paradigms, and quantum phenomena. In order to take advantage of the promising properties of these heterogeneous systems, holistic study
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NIST only participates in the February and August reviews. With the advent of the silicon drift energy dispersive X-ray detector (SDD/EDS), enhanced opportunities for high precision and accuracy measurements have opened up[1]. Unfortunately though, few people are making full use of the...
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301.975.3438 Description NIST has developed an integrated measurement services program for forensic and cannabis (hemp and marijuana) laboratories to help ensure the quality of routine analysis of cannabis plant
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-and-quality-control-materials-metqual-program key words Metabolites; Metabolic pathways; Mass spectrometry; Bioinformatics; Chemometrics; Multivariate statistics; Human health; Precision medicine
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measurements at 18-digit accuracy using an optical clock network. Nature 591, 564–569 (2021). https://doi.org/10.1038/s41586-021-03253-4 [2] Chave, A. D. (2019). A multitaper spectral estimator for time-series