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activity of individual cells over time. We have shown that such data can provide information about fluctuations in promoter activity and can be used to predict rates of state change in cell populations
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independent strategies (metagenomics, bioinformatics, synthetic biology) to determine microbial function from sequence data. Since this project is highly interdisciplinary, we are seeking applicants from
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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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resulting polyplex structure. Advancing data analysis methods and instrumentation are important to this opportunity. (1) McKinlay, C. J.; Vargas, J. R.; Blake, T. R.; Hardy, J. W.; Kanada, M.; Contag, C. H
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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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-based and data-driven prediction models are often impractical for operational use due to unrealistic assumptions, limited data availability, and prohibitive computational costs. To address
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-eddy simulation and direct numerical simulation of the phenomena. Topics of interest include algorithm development numerical combustion, scientific visualization, and data analysis. key words Buoyancy
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, and algorithm design for inferring conclusions from multiple sources of information. Uncertainty quantification and propagation is vitally important such autonomous workflows, as is the development
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design and data analysis at the NCNR using techniques such as reinforcement learning. key words AI; Machine Learning; Artificial Intelligence; Neutron; Eligibility citizenship Open to U.S. citizens level
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materials research and development by orders of magnitude, and it is a core capability and focus area for the Data and AI-Driven Materials Science Group, MMSD, MML. This research opportunity centers