177 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Dr" positions at NIST in United States
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-to illuminate the structural transformations that occur across phases. The optical characterization of biological molecules using vibrational spectroscopy supplies critical, detailed structural information
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or algorithms for detection of the drug signal in complex mass spectral data and chemometrics for identification or classification of drug(s) is also of high interest.Through this opportunity, collaboration with
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NIST only participates in the February and August reviews. Demand for mobile data, the implementation of new wireless devices, and an explosion of mobile users has stressed our telecommunications
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of protein therapeutics, because NMR spectra are sensitive to molecular shape and intermolecular interactions as well as chemical structure, and NMR can reproducibly probe this information at atomic resolution
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their enterprises. We are interested in projects that focus on the development and application of performance metrics, information models, test methods, and protocols to assess and assure the key attributes
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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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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
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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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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
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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