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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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. "Integration of housing and social vulnerability into an optimization model for community resilience planning", Lifelines 2021-22 Conference, University of California, Los Angeles, Jan. 31 – Feb. 11, 2022. 2
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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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technologies to manipulate biological macromolecules such as DNA, and the controlled degradation of tissue engineering scaffold or drug delivery materials. To optimize performance and to design new applications
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experimental data archives available at NIST/TRC and advanced multidimensional optimization techniques to generate self-consistent, predictive force-filed models applicable over wide range of conditions. key
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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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aptamer conformation changes. Method development efforts should focus on the incorporation of a robust and optimized experimental design aimed at assessing the sources of variability, repeatability, and
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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