167 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"BBMRI-ERIC" positions at NIST
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NIST only participates in the February and August reviews. This research opportunity is focused on developing advanced chemical characterization and analytical chemistry tools, data and research
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-based and data-driven prediction models are often impractical for operational use due to unrealistic assumptions, limited data availability, and prohibitive computational costs. To address
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-eddy simulation and direct numerical simulation of the phenomena. Topics of interest include algorithm development numerical combustion, scientific visualization, and data analysis. key words Buoyancy
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, and algorithm design for inferring conclusions from multiple sources of information. Uncertainty quantification and propagation is vitally important such autonomous workflows, as is the development
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materials research and development by orders of magnitude, and it is a core capability and focus area for the Data and AI-Driven Materials Science Group, MMSD, MML. This research opportunity centers
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are developing microfluidics to measure material properties and structure. Protein, polymer and surfactant solutions and suspensions and emulsions are being characterized using computer-controlled microfluidic
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Description We work with scientists in other NIST laboratories to develop tools for computer simulation and analysis of magnetic systems at the nanometer scale. Model verification is achieved by comparison
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; Microelectronics; Machine learning; Data informatics; Physics; Terahertz; Metrology; Chemistry; Materials engineering;
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these factors can have strong spatially-dependent influences on field evaporation conditions, the quantitative interpretation of 3D elemental atomic reconstructions of (conventional) atom probe data can be quite
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characterization, microstructural analysis, modeling, and/or data science to reach out and apply, as a variety of perspectives will be invaluable in advancing our understanding of material behavior and design. We