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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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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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alloys, carbon-based composites, and solid-state-biomolecule hybrid structures. Our data-driven development uses cheminformatics methodologies combined with machine learning methods to produce predictive
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characterizations of the process-structure and structure-property relations, and the development of database content. The developed models, knowledge, data, and software will be used to further the NIST effort to
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) and have a background in imaging, image analyses, machine learning, and software engineering. References: (1) A. J. Brooks et al, “Neutron interferometry detection of early crack formation caused by
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, “Development and utilization of a database of infilled frame experiments for numerical modeling”, Journal of Structural Engineering, (2020), 146 (6). Siamak Sattar, “Evaluating the Consistency between
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@nist.gov 301.975.4310 Description The Fire Research Division of NIST’s Engineering Laboratory develops and maintains a computational fluid dynamics software package for modeling building and wildland fires
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range from nano to cement to models that exhibit complex dynamics. This research will build on our open source software that runs on a linux desktop as well as immersively. Opportunities exist for (1
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RAP opportunity at National Institute of Standards and Technology NIST Augmented Intelligence for Semiconductor Manufacturing Location Engineering Laboratory, Intelligent Systems Division
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(NIST Engineering Laboratory), and partners at other national laboratories and academic institutions. Subsequent testing, characterization, and modeling will be performed in our world-class mechanical