172 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"ISCTE-IUL" positions at NIST
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NIST only participates in the February and August reviews. This research opportunity is focused on developing advanced chemical characterization and analytical chemistry tools, data and research
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property data primarily intended for model development that investigate how the molecular size, molecular structure, and polarity of fuel constituents impacts their thermophysical properties. Measurements
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that are all highly polar and associating. Therefore, biofuels present an enormous challenge for property science. Their successful implementation demands accurate property data for actual fuel mixtures, which
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disseminating sorbent material property data and measurement "best practices." Specific research activities include utilization of state-of-the-art techniques and establishing protocols for measuring reliable
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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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information over quantum networks but can also be used for high-efficiency optical modulation and microwave sensing applications. Most devices of interest here will convert microwave photons to acoustic waves
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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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. 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