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of prior physics knowledge into the data analysis, including both physics theory and databases of experimental and computational materials property data. We are particularly interested in using
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correlations and prediction methods. The program will build on our existing efforts using Quantitative Structure-Property Relationship (QSPR) methodologies and modern machine learning methods (support vector
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. Nanotechnology (26)44: 444002, 2015 Mueller T, Kusne AG, Ramprasad R: Machine Learning in Materials Science: Recent Progress and Emerging Applications. Reviews in Computational Chemistry, June 2015 Materials
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requires expertise in Computer Science, Statistics, or a similar field. Experience with machine learning, genetics, and/or bio-informatics is strongly preferred. The postdoc will work together and within a
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Description This program is designed to support the design, construction, and operation of high-performance sustainable buildings with good indoor environments and low levels of energy consumption. This goals
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applications, the sensitivity of cryogenic instrumentation far surpasses that of conventional room temperature electronics. Consequently, NIST has a large program to develop detectors that operate
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structural techniques for probing the interface, such as SEIRAS and STM, with computational methods to develop new electrochemical models. The computational work focuses on combining DFT methods
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, scattering, etc. Concurrently, computational modeling will be used to predict both structure-property relationships and degradation rates based on the number and distribution of the monomers with
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driven flows; Combustion; Computational fluid dynamics; Fire modeling; Heat transfer; Large eddy simulation; Numerical combustion; Thermal radiation; Turbulent flows; Eligibility Citizenship: Open to U.S
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the potential of quantum states of light for advanced measurements and computation, integration in a chip-scale nanophotonic environment is required. In particular, the integration of single-photon sources with