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. Work will emphasize the development and analysis of advanced methods in areas such as sparse signal recovery, compressed sensing, and statistical estimation, with a particular focus on their role in
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on the development of continuum and discrete, stochastic mechanical models of ordered cellular structures and understanding the role of order in pattern formation. The project is in close collaboration with
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. The research focus is on developing and applying first-principles-based and data-driven computational methods to understand multiscale processes and accelerate chemical discoveries for renewable energy
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, focused on the electrochemical conversion of carbon dioxide to ethanol. Supported by an externally funded grant, this role offers a unique opportunity to contribute to the development of transformative
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Davis Educational Foundation. The project tests whether real-time assessment and feedback during cooperative education placements accelerate students' development of durable skills — critical thinking
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and efficiency of life sciences research. Developing the algorithms, infrastructure, and governance necessary for such analysis can simultaneously enhance hypothesis generation, computational modeling
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machine learning techniques to raw ultrasound data. The project involves developing novel algorithms that integrate physics, engineering, and AI to extract meaningful and clinically relevant information
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. Developing the algorithms, infrastructure, and governance necessary for such analysis can simultaneously enhance hypothesis generation, computational modeling, and post hoc support for laboratory studies and
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catalytic reactors to measure activity, selectivity, and kinetics for targeted thermo- and/or electrocatalytic reactions Develop and run closed-loop autonomous workflows that couple automated synthesis, high
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on the design and development of mathematical, probabilistic, and statistical frameworks for drawing inferences from complex biological data in collaboration with scientists at the Snow Centre for Immune