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DeCost brian.decost@nist.gov 301.975.5160 Description Trustability and physical interpretability are critical requirements for the development of robust and sustainable machine learning systems needed
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, Riethmann T, Feistel R, Harvey AH: New Equations for the Sublimation Pressure and Melting Pressure of H2 O Ice. The Journal of Physical and Chemical Reference Data 40: 043103, 2011 Outcalt SL, Laesecke AR
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measure structural changes as the agents go from their biologically active to their biologically inactive forms. As analytical methods become available, studies of the physical and chemical processes
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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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a PhD in Mechanical Engineering, Materials Science or Physics who has expertise in multi-physics simulations, non-linear FEM, inverse analysis and constitutive modeling. Prior experiences in modeling
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identification process and enable end users to select the appropriate tools for their applications. Combining existing and new technologies is encouraged. The proposal may include a combination of benchwork and
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physical properties relevant to understanding and predicting climate change; the measurement of the chemical kinetics and mechanisms of elementary gas-phase reactions and model systems for heterogeneous
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: chemical understanding of emerging commercial technologies, studying beneficial reuse of industrial and process wastewaters, supporting commodity authentication, seafood safety and exposure science, and
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function of the hydrogen concentration. This information provides insight into the chemistry and physics of hydrogen adsorption/absorption, which is then used to identify the most promising avenues
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and Techniques 59(1): 188, 2011 Database; Microelectronics; Machine learning; Data informatics; Physics; Terahertz; Metrology;