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measurements from incident electrons through to detected X-rays Spectrum processing (particularly low energy lines) Weights of lines and other critical physical parameter measurements Measurement optimization
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relate to and inform uncertainty estimates. Our current research addresses such problems by combining physics with tools from applied analysis, probability theory, asymptotics, optimization, and numerical
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NIST only participates in the February and August reviews. Computer-based tools, including the NIST Alternatives for Resilient Communities model, or NIST ARC, are being developed to support
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DFT, beyond-DFT, and experimental techniques. We are also interested in developing both forward and inverse machine learning models to accelerate and optimize the design processes. We work in close
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-acquisition circuitry, and signal-processing/pattern-recognition algorithms. The sensors must be tailored for the particular nature of a given chemical or biochemical measurement problem by optimizing and
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our understanding of the fundamental limitations of detectors and sources; development of new ways to package detectors, sources, and components optimized for few photon operation; and developing new
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NIST only participates in the February and August reviews. In many application areas, materials development increasingly involves manipulating the local atomic order to optimize properties
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. Chemical engineers constantly need reliable property data for process design development and optimization. This information is predominantly coming from scientific publications. Thousands of papers
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acoustically isolated with a phononic crystal, providing both high mechanical and optical quality factors. Through optimal optical and mechanical design, this approach could allow for quantum control in
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products. Assay development efforts should focus on the incorporation of a robust and optimized experimental design aimed at assessing the sources of variability, repeatability and reproducibility