171 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Dr"-"L2CM" positions at NIST
Sort by
Refine Your Search
-
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
-
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
-
independent strategies (metagenomics, bioinformatics, synthetic biology) to determine microbial function from sequence data. Since this project is highly interdisciplinary, we are seeking applicants from
-
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
-
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
-
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
-
-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
-
-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
-
, 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
-
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