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, and complete products of combustion) will be performed. Comprehensive data sets of this type have not been previously reported for full-scale enclosure fires. The chemical data will be augmented by
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. Sriram sriram@nist.gov 301.975.3507 Description Systems biology involves gathering comprehensive sets of data that define and quantify the elements of a particular biological system and computationally
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are manufactured on one production line, the control-process-quality relationships are not fully understood based on quantitative data analysis. Understanding the control-process-quality relationships will enable
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machine tools that are self-aware via on-machine measurements and diagnostics, to track the machine’s performance health. Furthermore, solutions must be non-invasive, data-rich, inexpensive, and accurate
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) motivates research in Knowledge Representation and Reasoning (KRR). The successful applicant would have an opportunity to apply their expertise in semantic web, linked data, and knowledge graphs to the field
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shocks and stressors). This research effort relies extensively on modeling and optimization, with consideration for field data collection, and statistical and geospatial data analysis. Informed by
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over time. Stem cell populations can be highly heterogeneous and can exhibit complex responses. Using quantitative imaging data on large numbers of live cells over time, we can construct potential
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of products that incorporate billions of nanoscale features. Recent publications report our scatterfield microscopy techniques in which extensive electromagnetic modeling, instrument characterization, and data
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not be effectively analyzed. DIC data are combined with finite element modeling to analyze non-uniform stress and strain states within samples during dynamic loading, and in some cases to deduce
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of the uncertainties encountered in the physical experiments. These computational methods have been applied to nanorheology in photopolymerization 3D printing [1], human breath research for forensic and clinical data