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are interested in using Machine Learning and AI techniques to enable autonomous, AI-Driven, experimental research. There are many aspects of this nascent field that require further development. This includes
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evolution. The Group aims to advance fundamental understanding, improve predictability for design, ensure reproducibility and comparability, and facilitate scalability for real-world applications
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Michael Pettibone john.pettibone@nist.gov 301.975.5656 Description Detection, characterization and temporal evolution of metal nanoparticles is undergoing environmental transformations. Within
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against experiment and by development of reference problems. Important issues include controlling round-off and truncation error to obtain high accuracy solutions in complex, large scale simulations, and
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on the science that will underpin the development of the needed metrology to close this gap. The ideal candidates would have some understanding of high frequency electrical characterization, as well as substantial
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communities impact all aspects of the world in which we live, and our relationships with surrounding microbial populations can have negative and positive impacts on the survival of both. The development
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quantitation of the effects of environmental context and evolution. The Group aims to advance fundamental understanding, improve predictability for design, ensure reproducibility and comparability, and
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, Lawrence Livermore National Laboratory, June 28, 2013 https://www.osti.gov/servlets/purl/1090008 Frenkel, M.; Dong, Q.; Wilhoit, R. C.; Hall, K. R. TRC SOURCE Database: A Unique Tool for Automatic Production
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-sensitive focal plane arrays for use in CMB measurements. The sensor elements are superconducting transition-edge sensors that are read out by multiplexed SQUIDs. The research will involve the development
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@nist.gov 301 975 2093 Description This opportunity focuses on the development of analytical methods and/or data processing techniques that could be used to advance drug detection and identification (or drug