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of cutting-edge statistical methods and machine learning algorithms inspired by massive healthcare datasets. Key Responsibilities Develop innovative statistical methods and machine learning algorithms
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-analysis project, Bayesian background with experience in hierarchical modelling and mixed effect models is preferred. The second project, knowledge in survival analysis and machine learning is desired
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in the development of cutting-edge statistical methods and machine learning algorithms inspired by massive healthcare datasets. Key Responsibilities Develop innovative statistical methods and machine
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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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Resident/Fellow education. Preferred Experience Having, or willing to learn, the intricacies of grant application processes. Skills Proficient in the use of computer databases and PowerPoint, Excel, and Word
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modelling and mixed effect models is preferred. Knowledge in survival analysis, machine learning, and bioinformatics in omics data is desired. Consideration of applications will begin immediately, and will
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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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bioinformatics area) position is open at Duke University School of Medicine in the lab of Dr. Yi Zhang starting Jan 2024 or later. The ideal candidate will develop novel statistical and machine learning methods
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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