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Consortium led to the development of the first NIST RMs in this class, with widely-used benchmark germline variant calls for seven human cell lines [1]. Artificial intelligence and machine learning hold
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physical sensing, quantum science, communications, and dynamic spectroscopy. We have developed novel approaches to comb generation [1], spectral translation [2], and their use to interrogate cavity
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@nist.gov 301.975.4127 Description This research is centered on the development and application of analytical methods to the characterization of nanomaterials. Opportunities exist to study the composition
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causes of data variability to improve product quality and reproducibility [1]. Simulation Modeling: Developing theoretical and mathematical descriptions of physical phenomena, including both physics-based
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are seeking researchers to contribute to the development and application of advanced measurement and automation techniques for exploring processing-structure-property-performance (PSPP) relationships in
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quality (p, ρ, T) measurements from 200 – 505 K, with pressures to 40 MPa. The speed of sound is a property that yields very powerful data for developing fluid equations of state (EOS), and we have two
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electronics. New materials are continually being developed for electronic applications, and accurate measurements of the electromagnetic properties of these often complex new materials is critical both
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. To facilitate recycling, there is a critical need to develop and refine advanced separation methodologies for common fiber blends, such as cotton, polyester, and elastane. Developing methodologies to recover and
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large proteins. Cultures with poor transfection efficiency do not generate enough signal for STD-NMR spectroscopy and this has hindered method development. We seek proposals aimed at determining
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NIST only participates in the February and August reviews. This opportunity focuses on the development and implementation of liquid chromatography mass spectrometry methods for the quantitation