79 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"Cardiff-University" 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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signal processing, fabrication, materials characterization, ultrasound physics, and fabrication. Qualified candidates will have some of these skills and be willing to learn. N.Orloff, J. Booth, et al
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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