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RAP opportunity at National Institute of Standards and Technology NIST Applied Optimization and Simulation Location Information Technology Laboratory, Applied and Computational Mathematics
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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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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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Description Mathematical modeling forms the basis for understanding, simulating, optimizing and controlling numerous scientific phenomenon and associated measurements. Mathematical models frequently take the
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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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of novel optical methods for nanoscale dimensional measurements using the NIST 193 nm Microscope: a newly upgraded, custom-built, world-class high-magnification optical imaging platform optimized
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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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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