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various government laboratories to elucidate mechanisms of protein binding to BLMs. We comprise researchers with a broad range of expertise and are actively developing advanced biochemical and biophysical
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of materials under operational conditions improves fundamental understanding and accelerates development of highly-reliable materials and devices. Applicants will work to develop relevant test approaches
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NIST only participates in the February and August reviews. Research on photovoltaics focuses on the development of new and improved device characterization methods for various cell technologies and
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higher production rates of small to medium sized parts compared to powder bed fusion technologies. The goal of this research opportunity is to develop new methods for their integration into machine
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prevent a true function-by-design approach to development and manufacturing. We are interested in using analytical theory, large-scale molecular dynamics (MD) simulations, and density functional theory (DFT
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the world. With this production system, we are looking to augment our ability to rapidly answer science questions using the aggregated data volume. Additionally, we seek to develop and deploy new autonomous
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are not sufficiently accurate, or the methods are too expensive to accurately model sufficiently large systems. As a result, these computational problems are ideal for developing machine-learned potentials
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NIST only participates in the February and August reviews. Research focuses on the development and application of advanced multi-detector separation science techniques. Topics include characterizing
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conditions. For that purpose, Raman spectroscopy-enhanced indentation technique (RS-IT) is being developed at NIST which combined instrumented indentation with Raman spectroscopy to analyze in-situ
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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