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STFP Home sign in | focus RAP opportunity at National Institute of Standards and Technology NIST Machine Learning-driven Autonomous Systems for Materials Discovery and Optimization Location
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Laboratory, Materials and Structural Systems Division opportunity location 50.73.11.B5285 Gaithersburg, MD NIST only participates in the February and August reviews. Advisers name email phone Xiaohong Gu
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thermophysical properties influence the injection and combustion characteristics of the engines they are used in, which ultimately impacts their efficiency. The thermophysical properties of fluids group at NIST
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the possibility of accelerating materials research and development by orders of magnitude, and it is a core capability and focus area for the Data and AI-Driven Materials Science Group, MMSD, MML
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highly selective chemical separations. Our group is working on a dual-track project to expand this class of materials, and the successful candidate will contribute to either the computational
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particularly interested in the role of polymer architecture, such as network structure, backbone stiffness and side group flexibility, on the rate-dependent elasticity and fracture toughness of extremely tough
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calibration exist. The Nuclear Medicine Project within the Radioactivity Group at NIST addresses some of these hurdles in an effort to provide the foundations for absolute quantitation in imaging. NIST
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Keenan [email protected] 303.497.3665 Karl Francis Stupic [email protected] 303.497.4564 Description Neural net systems have been developed to process magnetic resonance imaging (MRI) data
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of disordered metallic systems and are currently being developed for a variety of ordered metallic systems. These composition dependent mobility descriptions can then be used in conjunction with
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develop hardware based neuromorphic systems using emerging devices including Josephson junctions, magnetic Josephson junctions, and spintronic components. Many of these technologies are neuromorphic at the