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systems for accelerating computational catalysis and experimental design. The successful candidate will contribute to building AI-native frameworks that combine first-principles modeling, machine-learning
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Job Description Applications are invited for a National Science Foundation funded (LEAP HI Program #2051685), Postdoctoral Associate position with the System Performance Laboratory (SPL
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and collaborative researcher with expertise in engineering, materials science, or related computational fields to contribute to our research program focused on transport packaging optimization and
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computational approaches, with emphasis on physics-informed or mechanics-informed modeling. • Experience with the manufacture of and testing of thermally modified wood properties Overtime Status Exempt: Not
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research program that works both within and at the boundaries of data science/econometric methods and the social sciences to discover, create, and disseminate new knowledge. The successful candidate will
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-based models, and remote sensing technologies. Required Qualifications • Ph.D. in Civil or Environmental Engineering, Hydrology, Data Science, Geosciences, Computer Science, or a related field. PhD must
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supervision of Prof. Yingda Cheng on computational methods and modeling for kinetic equations. The research conducted will involve development of numerical methods, development and analysis of reduced order
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. - Strong proficiency in machine learning, optimization algorithms, and computational modeling applied to construction systems. - Experience with designing and conducting experimental studies to evaluate