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employing standard and advanced statistical methods, including machine learning algorithms. The incumbent will also pursue independent research interests, secure extramural funding, and contribute to academic
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Military Operational Neuropsychological Assessment (VRMONA) study that will conduct and support human subjects research by setting up and conducting study tasks in lab. The candidates will adhere to study
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care, and give direction to community efforts to improve behavioral health. We are currently looking for a Research Technician I to support the Virtual Reality Military Operational Neuropsychological
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, uses and maintains scientific equipment needed to carry out the research tasks. Conducts library and computer searches for research; modifies established protocols to meet the needs of the project and
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Qualifications PhD in data science, biomedical informatics, computational biology, neuroscience, or related field. Demonstrated experience in analyzing real-world medical data and applying machine learning
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.** William “Scott” Killgore, PhD, is a Professor of Psychiatry, Psychology, and Neuroscience that joined the University of Arizona July 1, 2014. He brings an active research program from Harvard Medical School
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. Demonstrated record of interdisciplinary collaboration across environmental or related fields. Demonstrated capacity or potential in quantitative methods, data science, machine learning, remote sensing, and/or
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-Speciality Location Main Campus Address Tucson, AZ USA Position Highlights The Department of Electrical and Computer Engineering invites applications for a limited term adjunct faculty position to teach ECE
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work will focus on problems in controls, machine learning, image reconstruction, wavefront sensing, and instrument development and test. As time permits, you will be encouraged to conduct your own
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profiles, to identify treatment response patterns, subtypes, and critical intervention windows that reduce Alzheimer's risk Disease (AD) risk. This position will involve applying machine learning, deep