217 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Newcastle University" positions at NIST
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Application of Artificial Intelligence Techniques for Acquisition and Analysis of Thermophysical and Thermodynamic Property Information NIST only participates in the February and August reviews
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NASA's ER-2 aircraft, which fly at an altitude of 21 km above sea level. These data will serve as tie points for new and existing models of the lunar spectral irradiance. Links: Air-LUSI -- https
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absorption fine structure), development of data-analysis approaches and computer software for simultaneous structural refinements using multiple types of data combined with ab initio theoretical modeling
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2178: Baseline Control Systems in the Intelligent Building Agents Laboratory. doi: https://doi.org/10.6028/NIST.TN.2178 . building control; intelligent agents; optimization; data analytics; machine
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clocks used in the past, creating “gappy” data which often strain, or outright violate, the assumptions underlying the statistical models currently used. This project centers around investigating and
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mass spectrometry; and advanced chemometric tools for the analysis, interpretation, and comparison of complex metabolomic data sets. https://www.nist.gov/programs-projects/metabolomics-quality-assurance
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RAP opportunity at National Institute of Standards and Technology NIST Leveraging Artificial Neural Networks for Enhancing GC and LC-MS Metabolomics Data Interpretation and Integration Location
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jason.widegren@nist.gov 303.497.5207 Description https://www.nist.gov/programs-projects/electric-acoustic-spectroscopy-intermolecular-interactions-solution#OnChip NIST’s Material Measurement Laboratory (MML) and
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testing novel MOF materials for applications in carbon capture (https://doi.org/10.1016/j.xcrp.2022.101063). Successful candidates must have a background in MOF synthesis and characterization. Special
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. Bandyopadhyay, B. Heer, Additive manufacturing of multi-material structures, Mater. Sci. Eng. R Reports. 129 (2018) 1–16. https://doi.org/10.1016/j.mser.2018.04.001. [2] J. Guo, R. Floyd, S. Lowum, J.-P