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methods for disease prediction, precision medicine, and clinical decision support. Applicants must have a Ph.D. in biomedical informatics, computer science, data science, biomedical engineering, statistics
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
$62,000- $70,000 Proposed Start Date Estimated Duration of Appointment 12 Months Position Information Be a Tar Heel! A global higher education leader in innovative teaching, research and public service
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computational methods for the control, optimization, and coordination of complex dynamical systems. The work will emphasize applications to multi-agent systems, including robotics, autonomous systems, and
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) Laboratory. The group develops clinically grounded artificial-intelligence and data-science methods to derive scalable digital biomarkers from echocardiography, cardiac computed tomography, electrocardiography
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. The project will involve drone optimizations, computer vision, closed-loop control, multi-modal sensor fusion, machine learning, and transfer learning methods. The candidate will also work in collaboration with
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health field. Physical Requirements Repetitive motion typical of desk work (sitting/bending/typing/twisting) Prolonged exposure to computer screen time Lifting/pushing/pulling up to 25lbs Shift Monday
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project milestones and contribute to technical reports and project updates. • Prepare manuscripts, presentations, and other scientific materials describing research methods and findings. • Prepare materials
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and the Behavioral Health Innovation and Dissemination Center (BHIDC) at the University of Utah are seeking a highly motivated postdoctoral research fellow with expertise in quantitative methods
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computer science, data science, statistics, bioinformatics, electrical engineering, computer engineering, biomedical engineering, mechanical engineering, operations research, robotics, computational neuroscience
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should be motivated to develop AI methods and apply them to clinically or epidemiologically meaningful research questions. We are particularly interested in candidates with experience in longitudinal data