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Field
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. Position Overview The successful candidate will develop and apply advanced computational and machine learning methods to large-scale genomic, clinical, and imaging datasets, working across one or more of the
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. • Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization, and a high level of independence with a publication record to support these
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
students, and collaborate with AI faculty in vision, robotics, and machine learning across the department. Minimum Education and Experience Requirements Applicants should have, or expect to receive before
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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, STATA, SAS, or other statistical software; Exposure to pre and postprocessing of magnetic resonance imaging and/or magnetic resonance spectroscopy data or willingness to learn neuroimaging techniques
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learning Opportunity to support diverse and engaged student populations Innovative academic environment Comprehensive employee benefits and wellness resources Inclusive community that values diverse
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, predictive modeling, machine learning, and causal inference methods. Experience with claims-based or EHR-based phenotyping, variable construction, treatment pattern analyses, healthcare utilization studies
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that transforms current AI for Science paradigms focusing on multidisciplinary applications in biomolecular modeling and design, leading to a step-change in Scientific Machine Learning (SciML). They will be
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and assessing weld quality of mechanical parts in real-time by developing machine learning models that use sensor data and other tasks that are assigned to you. Core Responsibilities: Understanding
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substantial intellectual independence and opportunities to collaborate closely with machine learning scientists developing predictive models of cellular function. Machine learning experience is not required