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techniques. Developed models will be used to optimize processing and conventional alloy compositions for additive manufacturing. T. Keller, G. Lindwall, S. Ghosh, L. Ma, B.M. Lane, F. Zhang, U.R. Kattner
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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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learning with machine-controlled measurement tools for closed loop experiment design, execution, and analysis, where experiment design is guided by active learning, Bayesian optimization, and similar methods
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RAP opportunity at National Institute of Standards and Technology NIST Designing Liquid Scintillators for Optimal Light Yield, Pulse Shape Discrimination, and Neutron Sensitivity
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. The NIST channel sounding measurement team specializes in the development and use of instrumentation in the 10s of GHz based on phased array antennas that is optimized to capture dynamically evolving
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. The postdoc will develop machine learning algorithms to analyze phenotype and sequence data, as well as active learning algorithms to optimize and control experiments in directed evolution. This position
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to work on optimizing MPS designs (potentially with integrated, microfabricated sensors), developing new tissues-on-chips, developing MPS for new organ combinations, and testing drug candidates in
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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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, 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