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For predicting thermochemistry of small main-group molecules, quantum chemistry is sufficiently reliable that it can be used to settle experimental disagreements and to provide critical data that are not available
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of fit for purpose high-quality reference materials that can be used to help normalize and benchmark data from the various EV isolation and characterization methods is a key bottle-neck inhibiting
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characterization, microstructural analysis, modeling, and/or data science to reach out and apply, as a variety of perspectives will be invaluable in advancing our understanding of material behavior and design. We
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system, offers higher sensitivity to surface displacement (noise floor of ~ 0.1 pm) than quasi-static PFM (~ 10 pm). However, conversion of the experimental results to quantitative displacement data is
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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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. The postdoc will develop machine learning algorithms to analyze phenotype and sequence data, as well as active learning algorithms to optimize and control experiments in directed evolution. This position
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mechanics (Monte Carlo or molecular dynamics simulation, calculation of virial coefficients from intermolecular potentials, etc.) can be used to supply data where experiments are infeasible or unavailable
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NIST only participates in the February and August reviews. Demand for mobile data, the implementation of new wireless devices, and an explosion of mobile users has stressed our telecommunications
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. A key challenge is the integration of prior knowledge into the data analysis, including both device physics and material properties.We are primarily interested in photonic (e.g. silicon ring
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NIST only participates in the February and August reviews. The modern transmission electron microscope (TEM) is capable of atomic-resolution structural and chemical imaging. However, such data