79 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S"-"U.S" positions at NIST
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data analysis techniques, instrument and sample environment development, and simulation methods to compare to experimental results. We are particularly interested in the development of two techniques
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technique. Multimodal imaging combines information from two or more imaging modalities such as MRI, computed tomography (CT), positron emission tomography (PET), and ultrasound (US). These combined techniques
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experiments and datasets for model validation of multi-phase computation fluid dynamics (CFD), discrete element method (DEM), or data-driven modelling. Measurement of defect types and populations using micro- x
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sophisticated potential energy functions and adequate sampling to reveal the associated, intricate molecular details. In addition to being centrally important, high-quality experimental data (free energy
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of prior physics knowledge into the data analysis, including both physics theory and databases of experimental and computational materials property data. We currently run 10 diverse autonomous platforms
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, the property information demand from industry is much larger than the current rate of experimental data production. Consequently, there is an obvious need for accurate methods that can predict viscosity
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–structure–property relationship by data fusion: Bayesian co-regionalization N-dimensional piecewise function learning. Digital Discovery, 3(11), pp.2211-2225. Materials Genome Initiative; Machine learning
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understanding of materials. For communications technology, the lack of acoustic data limits the design of acoustic filter technology. For pharmaceuticals and chemical manfucaturers, there are important relaxation
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relative to community goals; and interdisciplinary research and modeling, data visualization, and programming (software architecture, web application development). Buildings; Infrastructure systems; Social