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methodologies generally, machine learning techniques, OR complexity analysis/nonlinear dynamics are particularly well-matched to the opportunity, but applicants with theoretical expertise related to compact
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methodologies generally, machine learning techniques, OR complexity analysis/nonlinear dynamics are particularly well-matched to the opportunity, but applicants with theoretical expertise related to compact
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: Bibliography and identification of the energy system components. Theoretical modeling and simulation of the flow systems. Data analysis. Assisting the PI and the sponsor in grant writing for other funding
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of Engineering with many fascinating projects that focus on applying advanced nonlinear theoretical, computational and experimental techniques to solve practical real-world problems in engineering and biomedicine
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Qualifications: A PhD in Mechanical Engineering required; successful PhD thesis defense with pending graduation will be considered Experience with theoretical modelling and data analysis using Machine Learning is
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to conduct research on two interdisciplinary research projects on transportation, environment, and health. The candidate is expected to work on data integration and fusion, develop theoretical and mathematical
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reports and presentations for project sponsor. Minimum Qualifications: PhD in Mechanical Engineering required. Experience with hardware development and instrumentation required, as well as experience with
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reports and presentations for project sponsor. Minimum Qualifications: PhD in Mechanical Engineering required. Experience with hardware development and instrumentation required, as well as experience with