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are not sufficiently accurate, or the methods are too expensive to accurately model sufficiently large systems. As a result, these computational problems are ideal for developing machine-learned potentials
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; Hyperspectral imaging; Data mining; Machine learning; Microspectroscopy;
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alloys, carbon-based composites, and solid-state-biomolecule hybrid structures. Our data-driven development uses cheminformatics methodologies combined with machine learning methods to produce predictive
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substances in a wide pressure and temperature ranges). We also possess significant computational resources necessary for successful implementation of molecular simulations and machine learning methods
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that are difficult to obtain via traditional casting and subtractive machining. At the same time, additive materials are subject to extreme conditions that can include repeated thermal cycling both near and beyond
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Kumar Banerjee dilip.banerjee@nist.gov 301.975.3538 Description The use of lightweight materials in vehicles will significantly increase fuel efficiency and cut emissions, but the auto industry lacks data
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quantitative biophysical studies of cells and optical biopsies of tissues. Ongoing research in this area involves: (1) microscopy with label-free contrasts involving auto-fluorescence, absorption, and scattering
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signal processing, fabrication, materials characterization, ultrasound physics, and fabrication. Qualified candidates will have some of these skills and be willing to learn. N.Orloff, J. Booth, et al
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measurement, learn microfluidic fabrication, measure novel materials (proteins, biomolecules, and nanoparticles), and gain hands-on experience with on-wafer calibrations. This project will begin with exploring