19 machine-learning "https:" "https:" "https:" "https:" uni jobs at University of Virginia
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, and MR spectroscopic imaging using machine learning; candidates with experience in these areas are encouraged to apply. PREFERRED QUALIFICATIONS: APPLICATION PROCEDURE: Apply online at https
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modalities Experience with signal enhancement, machine learning, or data-driven imaging analysis Track record of publications in high-impact scientific journals, conferences or patents. Experience contributing
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toward their desired path after high school—from exploring careers to assisting with applications. Learn more about AdviseVA here: https://adviseva.virginia.edu/ AdviseVA considers a variety of
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. They will serve as the financial liaison with the Dean’s Office of the Engineering School. They should make meaningful contributions on a daily basis and be willing to acquire new skills as the needs
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. The University of Virginia School of Engineering and Applied Science Department of Electrical and Computer Engineering seeks qualified candidates to teach the Applied Circuits undergraduate course in Electrical
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Energy Spectroscopic Instrument (DESI), Simons Observatory (SO), the Legacy Survey of Space and Time (LSST). We are also interested in candidates who will apply Artificial Intelligence/Machine Learning
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data from mobile, wearable, and environmental sensors. The successful candidate will create and deploy models that integrate machine learning, signal processing, and large language models (LLMs) to infer
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to study chromatin and gene regulation in mammalian cells and human disease systems. Current ongoing projects include: statistical modeling and advanced machine learning/AI method development for predicting
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machine learning. The research associate is expected to conduct research on human-fires interactions in built environment. QUALIFICATION REQUIREMENTS: A PhD in atmospheric science, geography, environmental
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package comprising predictive software, data analytics, and machine learning. The candidate will work closely with and report to the Software Architect of the research Center led by Dr. Xinfeng Gao. As a