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RAP opportunity at National Institute of Standards and Technology NIST Materials Discovery Using Synchrotron Radiation, Machine Learning, and Artifical Intelligence Location Material Measurement Laboratory, Materials Measurement Science Division/Brookhaven Lab opportunity...
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-process densification. Complementary computational model simulation capabilities are also available. [1] J. Ilavsky, F. Zhang, R.N. Andrews, I. Kuzmenko, P.R. Jemian, L.E. Levine & A.J. Allen; J. Appl
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provide a rigorous framework for the validation of novel computational methods. Computational methods on multiple scales, ranging from continuum and empirical molecular mechanics models to first-principles
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classification of scientific publications by their relevancy have been done at TRC. A successful applicant is expected to have a strong background in computer sciences, particularly in AI, NLP, and ML. No specific
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NIST only participates in the February and August reviews. Computer-based tools, including the NIST Alternatives for Resilient Communities model, or NIST ARC, are being developed to support
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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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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