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guiding materials measurement experiments to acclerate learning the synthesis-process-structure-property relationship. Machine learning methods include, but are not limited to, Bayesian inference
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the method impractical. We are exploring these state preparation ideas, as well as the metrology of the states produced and then once produced, the use of those states for high accuracy measurements. In
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. These materials systems may have far-reaching applications, extending from neuromorphic computing to compact multiple-input multiple-output antennas. By achieving the aims of this project, this Associate will
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characterization tools for closed loop experiment design, execution, and analysis, where experiment design is guided by active learning, Bayesian optimization, and similar methods. A key challenge is the integration
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MultiPhysics Measurements and Modeling for Microelectronics at Microwave and mm-Wave Frequencies NIST only participates in the February and August reviews. Performance, security, and reliability of microelectronics systems are critical issues for the continued robust growth of the US economy....
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Advanced Control Systems in Commercial HVAC Equipment in the Intelligent Building Agents Laboratory NIST only participates in the February and August reviews. Approximately 84% of the life cycle energy use of a building is associated with operating the building. Building owners also face...
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NIST only participates in the February and August reviews. Community Resilience Metrics The Community Resilience Program (https://www.nist.gov/community-resilience) is developing science-based
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RAP opportunity at National Institute of Standards and Technology NIST MEMS-Based Scanning Probe Microscopy Location Physical Measurement Laboratory, Engineering Physics Division opportunity location 50.68.31.B7380 Gaithersburg, MD NIST only participates in the February and August...
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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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-based and data-driven prediction models are often impractical for operational use due to unrealistic assumptions, limited data availability, and prohibitive computational costs. To address