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, neuroscience, or a related field. Preferred Qualifications: Prior research experience in medical image analysis is desirable. Prior experience in machine learning or deep learning, along with proficiency in
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machine learning and deep learning methods for medical image analysis. His research findings have been published in top-tier journals and conferences, e.g., IEEE TMI, MedIA, MICCAI, and CVPR. Job
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, and multi-omics data analysis. They also develop novel diagnostic and clinical applications in the field of comparative oncology deploying AI and machine learning. The best-qualified candidate will have
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-month faculty position at the rank of Research Assistant/Associate/Full Scientist in the Department of Computer & Information Science & Engineering. Initial appointment will be for one year with annual re
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group as well. The group's research interests include axion and dark matter phenomenology, gravitational wave phenomenology, BSM model building, applications of machine learning to particle physics, and
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interaction, human systems integration, and quantitative research methods. Some experience with qualitative methods or an interest in learning about these methods will be beneficial. This position will work
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-month faculty position at the rank of Research Assistant Scientist in the Department of Computer & Information Science & Engineering. Initial appointment will be for one year with annual re-appointment
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://biostat.ufl.edu/ ) is accepting applications for a fully-funded postdoctoral associate position. This position, available immediately, focuses on developing statistical, machine learning and deep learning methods
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, government sites, data subscriptions, and other sources Assist research team in extracting and organizing data for individual studies Explore machine learning and AI applications Learn about the HyperGator
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compliance, computer programming (Python and R preferred), data analysis and visualization, machine learning and artificial intelligence, wearable sensors, biomechanics, sports analytics, sports performance