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, automated neuroanatomical phenotyping, machine learning and advanced statistical approaches. The doctoral project will be developed jointly with the successful candidate and tailored to their
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strengths in applied mathematics, statistics, data science, dynamical systems, probability, machine learning, and mathematical modeling, the department provides an exceptional environment for
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simultaneously from both partners. Machine-learning algorithms will subsequently be developed, trained and tested to identify markers of reduced brain resilience during ageing. The project adopts an
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practices Course Description ARI 410 - Machine Learning CSC 375. (4) Study of machine learning, a subfield of artificial intelligence intersecting with statistics, cognitive science, information
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related fields. Prior lab experience is preferred but not required. Candidates with research experience in machine learning and artificial neural networks, are strongly encouraged to apply. The candidate
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neuroanatomical phenotyping, machine learning and advanced statistical approaches. The doctoral project will be developed jointly with the successful candidate and tailored to their methodological
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, machine learning, and mathematical modeling, the department provides an exceptional environment for researchers working at the intersection of quantitative science and human health. Faculty
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, automated neuroanatomical phenotyping, machine learning and advanced statistical approaches. The doctoral project will be developed jointly with the successful candidate and tailored to their
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Applicable: Several other collaborators in the Anesthesiology Department with significant expertise in Machine Learning/AI are available to help provide overall guidance and strategy to the postdoc
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, Cybersecurity, Data Science, Information Technology, Information Systems, Machine Learning, Business Administration, or a closely related field from a regionally accredited institution is required