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interested in applicants who use advanced quantitative methods, including computational modeling, machine learning, and/or analyzing structural and functional neuroimaging data. Specific activities may include
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 20 hours ago
interest in mental health, suicide prevention, health professions training, and/or military health, as demonstrated through any or all of the following: A record of scholarly publication and presentation in
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focusing on multi-omic integration analytics, machine learning, and/or AI. In addition to carrying out research, the successful candidate will be expected to apply for fellowship funding, contribute
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qualification; (ii) demonstrate research skills in imaging science and machine learning, particularly on image reverse engineering, fake image and video detection, statistical detection models and mathematical
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of at least some of the following: – Extensive independent research experience – Creativity and independence – Experience analyzing hyperspectral data and developing machine learning models - Genetic
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of induced pluripotent stem cell-based human disease models 2) Understanding the role of immunity during tumorigenesis as well as disease relapses in order to design novel inhibitors, CAR-T cell therapy and
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2). Vascular network engineering and angiogenesis 3). Applications of machine learning in cell and tissue engineering The Disease Biophysics Group is a creative, transdisciplinary group of engineers
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of novel computational/biostatistical/machine learning methods for the integration of multiple, diverse dataset and the synthesis of hypotheses around the molecular mechanisms that drive the co-occurring
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(kidney biopsy, serum, urine) for comprehensive biomarker profiling. Utilization of machine learning and image processing for advanced tissue analysis. The Herman B Wells Center for Pediatric Research
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research position under the supervision of Dr. Chris Smith Home | Chris Smith . The lab— in the Evolution, Ecology, and Behavior section—investigates machine learning approaches for spatial population