193 assistant-professor-computer-"https:"-"https:"-"https:"-"https:" positions at NIST
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determination of marijuana components, development of vapor measurement technology and canine training aid materials for opioids and improvised explosives, targeted and non-targeted screening of bulk samples and
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RAP opportunity at National Institute of Standards and Technology NIST Machine Learning Driven Autonomous Metrology System Location Physical Measurement Laboratory, Sensor Science Division opportunity location 50.68.51.C0577 Gaithersburg, MD NIST only participates in the February and August...
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simulation techniques. In addition, simulations and examination of the overall separation process may require computational studies across multiple length scales. key words Modeling; Nanotube; Molecular
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NIST only participates in the February and August reviews. Research on photovoltaics focuses on the development of new and improved device characterization methods for various cell technologies and the improvement of measurement science to reduce uncertainties associated with the power rating....
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employed. This involves the computational determination of 3-D features of a specimen from a series of their 2-D projections. By carefully preparing the specimen, designing the experimental acquisition, and
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experiments and datasets for model validation of multi-phase computation fluid dynamics (CFD), discrete element method (DEM), or data-driven modelling. Measurement of defect types and populations using micro- x
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RAP opportunity at National Institute of Standards and Technology NIST Magnetic Resonance in Industrial Applications Location Physical Measurement Laboratory, Applied Physics Division opportunity location 50.68.62.C0945 Boulder, CO NIST only participates in the February and August...
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of solvation, redox potentials, pKa, spectroscopic observables, enzyme kinetics, etc) for these processes provide a rigorous framework for the validation of novel computational methods. Computational methods
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of prior physics knowledge into the data analysis, including both physics theory and databases of experimental and computational materials property data. We currently run 10 diverse autonomous platforms
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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