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some overlapping measures in the individual data sets and through the use of advanced analytic tools including machine learning and graph theoretics, one can discover multiple developmental pathways in
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and photonics Neuromorphic computing, artificial intelligence and machine learning Quantum and 2D materials technologies & systems Micro and nanoelectromechanical systems (MEMS/NEMS) Electromagnetics
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candidates whose research, teaching, and community engagement efforts contribute to robust learning and working environments for all students, staff, and faculty. We invite individuals who will join us in our
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, synaptic growth, brain network organization and connectivity, cognitive function) Using advanced neuroimaging and/or machine learning techniques to understand the connection between physical activity
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and photonics Neuromorphic computing, artificial intelligence and machine learning Quantum and 2D materials technologies & systems Micro and nanoelectromechanical systems (MEMS/NEMS) Electromagnetics
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information exchange (HIE) Natural language processing in clinical/biomedical domains Mobile health, digital health, human–computer interaction in health Learning health systems, community health informatics
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antibiotic resistance, including microbiology, immune engineering, and host–pathogen interactions - Biohybrid and bioinspired materials and biological–synthetic interfaces - Bio-machining, bio-manufacturing
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fields. A PhD degree in Artificial Intelligence, Informatics, Computer Science or related fields is preferred to teach at the graduate level. The ideal candidate will possess a minimum of twoyears of
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programs at the BS, MS, and PhD levels. Candidates must be tenured and demonstrate a strong scholarly record of effective, well-reviewed teaching, a scholarly record of externally-funded research, exhibited