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performance modeling capabilities that simultaneously consider multiple performance aspects, robust IAQ and other performance metrics, and measurement methods, sensors, and data to evaluate and verify building
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NIST only participates in the February and August reviews. More rigorous and advanced methods for the analysis of evidence are needed in forensic science and have been called for by the National
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scaled up to handle large numbers of samples in massively parallel, low-cost analysis systems. Before such systems can be realized, the electromagnetic response of biochemical samples must be understood in
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manufacturing methods such as additive manufacturing (AM) and post-process densification. The operative scale range for the void and phase microstructures of relevance extends from the micrometer scale down
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, robotics), cyberinfrastructure (e.g., databases, high-performance computing, collaboration tools), and humans (e.g., scientists, engineers, students, managers). The recent interest in Explainable AI (XAI
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via laboratory testbeds, numerous machining centers, and a nanoscale science center. Furthermore, NIST provides computational resources and has an interest group for AI that regularly meets, giving
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for achieving recovery-based objectives, (3) computing the collapse risk of new and existing buildings and infrastructure systems, (3) developing improved nonlinear modeling capabilities to evaluate the response
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Ravel bruce.ravel@nist.gov 631.344.3613 Description Develop methods of applying machine learning and artificial intelligence to synchrotron experimentation. This opportunity will be focused on operations
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NIST only participates in the February and August reviews. This research opportunity centers on advancing experimental measurement methods to quantify complexes formed between charge-altering
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consideration will be made to candidates with experience in automation or machine learning. The postdoc will join a group which is focused on pioneering applications of modern machine learning methods, FAIR data