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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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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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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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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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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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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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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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: 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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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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, systems, and instrumentation Supervise and participate in the construction and/or assembly of experimental apparatus, systems, and instrumentation Set up instrumentation with control software Acquire data