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imaging and patient care. You will join a highly collaborative, interdisciplinary research environment focused on developing and applying cutting-edge machine learning and deep learning methods to
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this position will work as a member of an interdisciplinary team on research involving the use of statistical and machine learning methods for the development of Immune Digital Twins. There will be
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position will work as a member of an interdisciplinary team led by Dr. Colin Xu on Department of Defense (DoD)-funded research project involving the use of statistical and machine learning methods for
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are associated with the development of childhood allergic diseases. The candidate will develop machine learning–based biomarker prediction models to identify microbiome-derived signatures associated
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, probability theory, dynamical systems, statistical mechanics, machine learning or applied/computational mathematics. ● Demonstrated ability to carry out original mathematical derivations and/or develop
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Care and Hospice, Duke Health and Wellness, and multiple affiliations. Be You. Be Bold. Choose Duke. Postdoctoral Associate, Diagnostic Imaging & Machine Learning Be You. At Duke, we celebrate
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cardiac surgery outcomes research and predictive analytics using advanced statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data
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density in regulating these processes. The insights gained from these empirical studies will be paired with computational biology and machine learning to develop predictive AI models of how cells
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 22 hours ago
of safe and effective therapeutics. Position Overview: We are seeking multiple highly motivated Postdoctoral Research Fellow with expertise in systems pharmacology, machine learning, and/or data
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University of California, San Diego | Estancia de San Diego, Guanajuato | Mexico | about 12 hours ago
the topic of machine learning-based ocean state estimation to begin in Spring/Summer 2026. The successful applicant will develop and implement data-driven tools, underpinned by scientific machine