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disseminating sorbent material property data and measurement "best practices." Specific research activities include utilization of state-of-the-art techniques and establishing protocols for measuring reliable
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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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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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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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Salipante paul.salipante@nist.gov 301 975 2820 Description Organ on a chip aims to be a more efficient way to gather pharmacokinetic data. To more accurately monitor the performance and health of some organs
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; DNA sequencing; De novo assembly; Machine learning; Reference materials; Precision medicine; Data science; Artificial intelligence Eligibility citizenship Open to U.S. citizens level Open to
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of mixed-gas adsorption, and the measurement of related physicochemical properties of porous materials. The FACT Lab seeks to improve the adsorption sciences by providing reference data using reference
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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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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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generation magnetic data storage. Research projects will include using X ray and neutron scattering to characterize the fidelity of the block copolymer structure to the template and computer simulations of