197 associate-professor-computer-"https:"-"https:"-"https:"-"https:"-"FCiências" positions at NIST
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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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RAP opportunity at National Institute of Standards and Technology NIST Immersive Visualization Location Information Technology Laboratory, Applied and Computational Mathematics Division
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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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., biomarkers, metabolites) must be evaluated using digital twins of breath device prototypes. Our digital twins are based on simulations using computational fluid dynamics (CFD) and computational fluid and
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quantitative analysis including rheology, DSC, scattering, etc. Concurrently, computational modeling will be used to predict both structure-property relationships and degradation rates based on the number and
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will be complemented by computer model simulations using available capabilities based on methods such as density functional theory (DFT). [3] [1] J. Ilavsky, F. Zhang, R.N. Andrews, I. Kuzmenko, P.R
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. The Associate will utilize advanced measurement capabilities including LC-MS/MS, LC-HRMS, GCxGC-MS, ICP-OES, and NMR to develop methodologies for chemical contaminant profiling, or next-generation sequencing
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quality of dietary supplements and ultimately reduce public health risks that could potentially be associated with the use of these products. key words Dietary supplements; Gas chromatography; Liquid
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to measure the physical processes that contribute to the biological activation and inactivation of proteins and membrane proteins such as conformational changes, aggregation, and/or macromolecular association
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Information Technology Laboratory, Applied and Computational Mathematics Division NIST only participates in the February and August reviews. Machine Learning (ML) and artificial intelligence (AI