23 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" research jobs at Villanova University
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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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Computer Engineering Contact Name: Helen Cook Contact Phone/Extension: 610-519-4970 Position Summary Information Job Description Summary: research on electrical and computer engineering topics; Assist with
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sampling of the parameter space of eclipsing binary observables, most notably photometric data from NASA’s Kepler and TESS missions. In parallel, the applicant will be given an opportunity to teach
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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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) training before or upon hire. Completion of Responsible Conduct of Research (RCR) training, as applicable to the role. Proficiency in computer literacy, including the ability to use standard office software
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project Requirements: · Experience using computer systems · Programming and coding an asset · Experience working with H5P, YouTube, MS Office, Adobe Acrobat and asset · 2-4 hours per week for a total of 20
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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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modeling. The undergraduate research assistant would work with a graduate student, research professor, and/or professor to complete both lab and field work to support stormwater research. Laboratory work may
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will join Andrej Prsa’s research team and work on the PHOEBE code , advancing our understanding of the processes in contact binary stars. In parallel, the applicant will be given an opportunity to teach
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successful applicant will join Andrej Prsa’s research team and work on cutting-edge theoretical and observational aspects of binary and multiple stellar populations. In parallel, the applicant will teach