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Machine Learning-driven Autonomous Systems for Materials Discovery and Optimization NIST only participates in the February and August reviews. We are developing machine learning-driven autonomous
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NIST only participates in the February and August reviews. We are developing machine learning algorithms to accelerate the discovery and optimization of advanced materials. These new algorithms form
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coefficients, and colossal magnetoresistance. Materials with optimal properties are generally solid solutions, often involving four or more different metal ions. Research opportunities exist in the systematic
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. Analytical biochemistry plays a significant role in optimization of the production process, testing and clearance of associated impurities, and characterization of product- and process-related variants
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shocks and stressors). This research effort relies extensively on modeling and optimization, with consideration for field data collection, and statistical and geospatial data analysis. Informed by
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catalysis; and the examination of reaction models used to optimize reaction efficiencies and pathways in chemical systems. A wide variety of diagnostic equipment is available including ultrasensitive cavity
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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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, characterize, and optimize interconnects between disparate chip technologies. Applicants will have the opportunity to learn high-demand skills for millimeter-wave technologies including calibration, integrated
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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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; narrow, symmetric peaks and flat baselines are readily obtained for most samples; signal averaging can be used to optimize signal-to-noise ratios; a wide range of molecular masses is accessible; and the