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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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analytical methods that are rapid, reliable, and sensitive. We are developing model cell expression systems based on rodent (Chinese Hamster Ovary Cells) that produce monoclonal antibodies at high levels
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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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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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, 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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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