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double-crystal diffraction instrument combined with a high-power, demountable x-ray source and a vacuum compatible hybrid pixel area detector. Decades of development of instrumentation for cutting edge x
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@nist.gov 301.975.4127 Description This research is centered on the development and application of analytical methods to the characterization of nanomaterials. Opportunities exist to study the composition
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been in development over the past 15+ years and their capabilities have grown significantly. An important effort within the LPBF community is the use of high-fidelity multiphysics models to predict melt
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. The composition explicit distillation curve method, developed at NIST, provides a unique approach to join fuel composition with the thermophysical properties. Of critical importance is the moiety family breakdown
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/ThreeBodyTB.jl), cluster expansion, classical potential development, and machine learning. In addition to work on specific problems, I work on developing new first principles-based modeling approaches, including
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seeks to develop the analytical capabilities and standards to support the measurement needs of the water measurement community and other governmental agencies that monitor and regulate water
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seeks to develop the analytical capabilities and standards to support the measurement needs of the water measurement community and other governmental agencies that monitor and regulate water
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and development of gene therapies. This NIST postdoctoral research opportunity focuses on developing robust protocols and refining measurement methods in infectious titer assays. Activities can include
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computer vision and machine learning. Our computational methods development has three primary goals. The first goal is continued support of expert-driven biomolecular structure determination by NMR, with
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systems. As a result, these computational problems are ideal for developing machine-learned potentials and computational workflows that integrate theoretical and sampling advancements. The NIST