79 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"IFM"-"IFM" positions at NIST
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that bolster state-of-the-art AI evaluation practices in the US government and the wider field of practitioners. Artificial Intelligence; Machine Learning; TEVV; AI Evaluation; Metrology; Psychometrics; Field
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catalytic turnover. Integrative modeling and machine learning have the promise of establishing new tools for combining computational and experimental data from HDX-MS and NMR to explain the dynamics and
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for data-driven (machine learning / artificial intelligence) applications. References Hoogerheide, D. P. et al. Structural features and lipid binding domain of tubulin on biomimetic mitochondrial membranes
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facilities include a commercial laser powder bed fusion machine, a commercial laser directed energy deposition machine, and several unique open-architecture laser powder bed fusion metrology testbeds with
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computer skills, including computer programming, are valued. Continued research is focused upon the emergence of complex event decision-making when there are concurrent and/or cascading risks involved
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even several million atoms on an ordinary computer. It links the different length scales smoothly and seamlessly.Such a model should be useful for many industrial applications of nanodiamnds
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are developing microfluidics to measure material properties and structure. Protein, polymer and surfactant solutions and suspensions and emulsions are being characterized using computer-controlled microfluidic
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Description We work with scientists in other NIST laboratories to develop tools for computer simulation and analysis of magnetic systems at the nanometer scale. Model verification is achieved by comparison
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intelligence and machine learning tools. 1. S. M. Chavali, J. Roller, M. Dagenais and B. H. Hamadani, Sol. Eng. Mater. Sol. Cells, 236, 111543 (2022). 2. B. H. Hamadani, Appl. Phys. Lett., 117, 043904 (2020
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Security Division opportunity location 50.77.31.B7615 Gaithersburg, MD NIST only participates in the February and August reviews. Advisers name email phone Lidong Chen lily.chen@nist.gov 301.975.6974 Meltem