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LSST. Projects will draw on new and archival observations from optical ground-based (e.g., Zwicky Transient Facility) and satellite (e.g., TESS) observatories, with potential supplementary data from X
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subject to continuing based on the availability of funding. The work will focus on the production of large polarimetric maps from the SOFIA data. The position is planned for two years, with a possible
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motivated with strong written and oral communication skills. Preferred Qualifications: Experience with first-principles calculations, DFT, Machine Learning, HPC. The individual is expected to actively pursue
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a data center waste heat recovery design tool. This tool enables the industry to determine how to best implement waste heat recovery based on location, operating conditions, and availability of third
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machine learning models to integrate real-time monitoring data. Collaborate with colleagues in computer science and computer engineering to develop models. Contribute to the dissemination of research
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2026) and teach (in fall 2027) a new Mendel Science Experience course on watershed science and sustainability. Additional teaching in subsequent semesters will include Mendel Science Experience courses
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Institutes of Health. The focus is on using concepts and techniques from statistical learning techniques, signal processing, machine learning, information theory and nonlinear dynamics. Villanova is a Catholic
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using real-time monitoring systems. Develop AI and machine learning models to integrate real-time monitoring data Collaborate with colleagues in computer science and computer engineering to integrate
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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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: Special Message to Applicants: You can learn more about Professor Xu’s work and the research groups’ activity from links below: Google Scholar Faculty Bio The Environmental Interfacial Chemistry (EIC) Group