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Description Mathematical modeling forms the basis for understanding, simulating, optimizing and controlling numerous scientific phenomenon and associated measurements. Mathematical models frequently take the
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our understanding of the fundamental limitations of detectors and sources; development of new ways to package detectors, sources, and components optimized for few photon operation; and developing new
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proposals to develop, optimize and deploy a headspace collection method to measure partition coefficients at physiological temperatures. We are especially interested in methods that target molecules
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the applied to the fundamental, covering such areas as understanding the evolution of the microstructure of nitride semiconductors; development of nanotemplates for patterned growth of nanowires; optimization
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and distributed control intelligence that can be applied to solve these problems through the application of machine learning, intelligent optimization techniques, automated fault detections and
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of novel optical methods for nanoscale dimensional measurements using the NIST 193 nm Microscope: a newly upgraded, custom-built, world-class high-magnification optical imaging platform optimized
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learning with machine-controlled measurement tools for closed loop experiment design, execution, and analysis, where experiment design is guided by active learning, Bayesian optimization, and similar methods
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qubits. These efforts are necessary to improve the scalability of the silicon spin qubit platform [2]. Initial efforts in autonomous tuning will focus on optimizing readout systems, shifting to the gate
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through the application of machine learning, intelligent optimization techniques, automated fault detections and diagnostics, automated commissioning tools, and management of local generation and flexible
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coefficients, and colossal magnetoresistance. Materials with optimal properties are generally solid solutions, often involving four or more different metal ions. Research opportunities exist in the systematic