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, (2) interpretation of experimental spectra, (3) development of semi-empirical methods, (4) studies of reactivity indices, (5) computational electrochemistry, and (6) chemical informatics. The explosion
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RAP opportunity at National Institute of Standards and Technology NIST Autonomous MOF Synthesis for Direct Air Capture Sorbents Location Material Measurement Laboratory, Materials Measurement Science Division opportunity location 50.64.31.C0890 Gaithersburg, MD NIST only participates in the...
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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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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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-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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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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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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, 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