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manufacturing processes, analyze the data with a fusion of metrological approaches and machine learning, and monitor and predict the performance of machines and their processes. Semiconductor manufacturers desire
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-driven recommendation systems that provide: Optimal and/or safest strategies for firefighting, ventilation, and search-and-rescue operations. Dynamic decision support that adapts to changing fire
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measurements with fundamentally limited performance. Sensor types of particular interest include accelerometers, gyroscopes, and acoustic and ultrasound detectors, which can be more sensitive than
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; narrow, symmetric peaks and flat baselines are readily obtained for most samples; signal averaging can be used to optimize signal-to-noise ratios; a wide range of molecular masses is accessible; and the
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patient’s target cells to treat intractable diseases, such as cancer and genetic diseases. Infectious titer is a critical measurement of viral vector function and potency. Such functional measures of viral
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NIST only participates in the February and August reviews. Manufacturing optimized devices that incorporate newly-emerging materials requires predictable performance throughout device lifetimes
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. The postdoc will develop machine learning algorithms to analyze phenotype and sequence data, as well as active learning algorithms to optimize and control experiments in directed evolution. This position
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, G. J., Herman, D. I., Giorgetta, F. R., Malarich, N. A., Baumann, E., Washburn, B. R., Newbury, N. R., Coddington, I., and Cossel, K. C.: Apportionment and Inventory Optimization of Agriculture and
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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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impediments to meeting the desired manufacturing and performance standards. Digital twins (DT) are being adopted in the AM industry to optimize the entire manufacturing process and enable products with high