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on advanced methodologies in abdominal imaging, particularly applications of machine learning and deep learning to medical image analysis. The lab aims to advance existing imaging techniques and develop novel
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method for analysis of behavioral and imaging data (e.g. computational modeling, machine learning, mixed-models) Strong background in neuroanatomy and neurobiology Interest in translating basic science
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-results · Proficiency in programming languages commonly used in computational biology, such as Python, R, or C++ · Experience with machine learning and deep learning methods · Excellent communication and
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Bayesian modeling and machine learning using large longitudinal biomedical data, including electronic health records and mobile health data. The position will be funded by Samuel I. Berchuck, PhD who holds
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system. For the meta-analysis project, Bayesian background with experience in hierarchical modelling and mixed effect models is preferred. Knowledge in survival analysis, machine learning, and
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program in pulmonary and transplantation research. It is located in Durham, North Carolina. Position Description: This position is to develop and apply innovative statistical, computational, and machine