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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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-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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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
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include the development of novel polymeric mechanical testing devices, novel adhesion blister testing devices, development of high-throughput screening devices, informatics, and data base development. key