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Description We work with scientists in other NIST laboratories to develop tools for computer simulation and analysis of magnetic systems at the nanometer scale. Model verification is achieved by comparison
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; Microelectronics; Machine learning; Data informatics; Physics; Terahertz; Metrology; Chemistry; Materials engineering;
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these factors can have strong spatially-dependent influences on field evaporation conditions, the quantitative interpretation of 3D elemental atomic reconstructions of (conventional) atom probe data can be quite
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characterization, microstructural analysis, modeling, and/or data science to reach out and apply, as a variety of perspectives will be invaluable in advancing our understanding of material behavior and design. We
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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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; 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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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
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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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, provide findable, accessible, interoperable and reuseable (FAIR) data sets, seek to improve sortation technology for homogeneous recycling feedstocks and work to advance the US circular economy. key words
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, metrology, and quantum information. Relevant reference examples as 2020: Kaufman et al., Science 345, 6194; Norcia et al., PRX 8, 041054; Norcia et al., Science 366, 6461 key words Quantum many-body systems