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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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://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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Information Technology Laboratory, Applied and Computational Mathematics Division NIST only participates in the February and August reviews. Machine Learning (ML) and artificial intelligence (AI
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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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NIST only participates in the February and August reviews. We are developing machine learning algorithms to accelerate the discovery and optimization of advanced materials. These new algorithms form
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RAP opportunity at National Institute of Standards and Technology NIST Computational Studies of Functional Oxide Materials and Devices Location Material Measurement Laboratory, Materials
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Computational Mathematics Division opportunity location 50.77.11.B7430 Gaithersburg, MD NIST only participates in the February and August reviews. Advisers name email phone Vladimir V Marbukh marbukh@nist.gov
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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 Paul Nathan Patrone paul.patrone
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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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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