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[email protected] 301.233.6503 Description The Analytical Chemistry Division has an ongoing program to improve the quality of analytical chemical measurements made in marine environmental research through
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adoption of this manufacturing technology—especially for critical applications that require qualification and certification—increasingly require that computational models and in-situ monitoring
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accurate measurements during emergencies, such as those encountered in pre- or post-detonation scenarios. The nuclear forensics program at NIST focuses largely on analytical method development, new and
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are critical for attaining measurement quality objectives and meeting the needs of the health and medical community. The isotope metallomics program at NIST focuses on analytical method development, rapid
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of the AFM probe-sample interaction. This research opportunity will focus on developing state of the art AFM instrumentation and computation modeling for 3D nanoscale property characterizations. In either
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variety of experimental techniques and computational models. key words additive manufacturing; phase transformations; processing-structure-property relations; metals; computational modeling; Eligibility
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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 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 [email protected]
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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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-and-quality-control-materials-metqual-program key words Metabolites; Metabolic pathways; Mass spectrometry; Bioinformatics; Chemometrics; Multivariate statistics; Human health; Precision medicine