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shocks and stressors). This research effort relies extensively on modeling and optimization, with consideration for field data collection, and statistical and geospatial data analysis. Informed by
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. Analytical biochemistry plays a significant role in optimization of the production process, testing and clearance of associated impurities, and characterization of product- and process-related variants
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relate to and inform uncertainty estimates. Our current research addresses such problems by combining physics with tools from applied analysis, probability theory, asymptotics, optimization, and numerical
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optimization for large-scale fabrication of diamond-based devices such as HMFETs and radiation detectors. In particular, the transfer doping of hydrogenated diamond is used to form 2D hole gas (2DHG) conducting
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increasingly clear that Machine Learning/AI are having great impacts across a number of fields of physics. This research opportunity revolves around applying these techniques towards optimizing experimental
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DFT, beyond-DFT, and experimental techniques. We are also interested in developing both forward and inverse machine learning models to accelerate and optimize the design processes. We work in close
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measurements from incident electrons through to detected X-rays Spectrum processing (particularly low energy lines) Weights of lines and other critical physical parameter measurements Measurement optimization
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catalysis; and the examination of reaction models used to optimize reaction efficiencies and pathways in chemical systems. A wide variety of diagnostic equipment is available including ultrasensitive cavity
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infrared spectroscopy, spectroscopic ellipsometry, contact angle measurements, and atomic force microscopy. Key aspects of this work involve examination and optimization of alternative functionalization
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guest binding in a flexible metal-organic framework under high pressure and room temperature", ACS Cent. Sci., 4, 1194–1200 (2018). R.-B. Lin, et al. "Optimized separation of acetylene from carbon dioxide