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increasingly clear that Machine Learning/AI are having great impacts across a number of fields of physics. This research opportunity revolves around applying these techniques towards optimizing experimental
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Computational Mathematics Division opportunity location 50.77.11.C0256 Gaithersburg, MD NIST only participates in the February and August reviews. Advisers name email phone Anthony Jose Kearsley anthony.kearsley
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these problems through the application of machine learning, intelligent optimization techniques, automated fault detections and diagnostics, automated commissioning tools, and management of local generation and
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Computer-based tools, including the NIST Alternatives for Resilient Communities model, or NIST ARC, are being developed to support community resilience planning [1, 2, 4]. The goal of NIST ARC is to
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to be used by every machine tool to enable optimized production of assets within manufacturing facilities. Augmented intelligence, which is the augmentation of traditional scientific intelligence with
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. The NIST channel sounding measurement team specializes in the development and use of instrumentation in the 10s of GHz based on phased array antennas that is optimized to capture dynamically evolving
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://jarvis.nist.gov/) infrastructure uses a variety of methods such as density functional theory, graph neural networks, computer vision, classical force field, and natural language processing. We are currently
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be generating terabytes of image data per day, there are several open research problems that would become the research focus of a postdoctoral candidate. The problems include (a) finding optimal
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NIST only participates in the February and August reviews. In many application areas, materials development increasingly involves manipulating the local atomic order to optimize properties
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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