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://jarvis.nist.gov/) infrastructure uses a variety of methods such as density functional theory, graph neural networks, computer vision, classical force field, and natural language processing. We are currently
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to this process is the understanding of phase relations, phase transitions, processing-structure, and structure-property relationships. Equally important to the modeling efforts are the experimental
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@nist.gov 301 975 2093 Description This opportunity focuses on the development of analytical methods and/or data processing techniques that could be used to advance drug detection and identification (or drug
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the construction process and correlates to the durability and service life of the composites. The goal is to understand the interplay between structure-properties-performance within these systems
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designs. In addition to fabrication and characterization of these measurement tools, we also develop new readout schemes, signal and data processing, control systems, and biomimetic surfaces to improve
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opportunities for interested candidates to develop advanced data processing techniques. key words microscopy; light scattering; imaging; interferometry; nanoparticles; gene delivery particles; biophotonics
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infrastructure, and experimental automation to materials characterization (metrology) methods across all portions of the structure-processing-properties-performance relationship. Specific group research focus
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Description Membrane proteins participate in virtually all interactions between cells and the surrounding media and play vital roles in all biological processes involving transport across cell membranes, charge
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lyle.levine@nist.gov 301.975.6032 Mark R. Stoudt mark.stoudt@nist.gov 301.975.6025 Description The extreme processing conditions of metal additive manufacturing create inhomogeneous materials that can include
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guiding materials measurement experiments to acclerate learning the synthesis-process-structure-property relationship. Machine learning methods include, but are not limited to, Bayesian inference