84 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at University of Minnesota
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, computer vision in the Division of Health Data Science (HDS) at the DOS. The position is an annually renewable professional academic appointment. Duties/Responsibilities: ● Risk predictive model for clinical
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computer science, or a field related to computational sciences. Must have a strong background in computer vision, artificial intelligence (AI), and/or wireless networking and systems, and related fields. Preferred
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or outside the University of Minnesota. The research will focus on applying, developing and implementing novel statistical and computational methods for integrative data analysis, causal inference, and machine
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the application of machine learning techniques (e.g., doc2vec, encoder models, multi-modal embeddings, large language models) to map concepts and their relationships, tracing how they change, merge, or diverge
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nuclear physics detectors. Experience analyzing data from high energy or nuclear physics experiments. Familiarity with Monte Carlo simulations. Familiarity with machine learning techniques. About the
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computer science programs (Chemical Engineering, Civil and Environmental Engineering, Computer Science, Electrical and Computer Engineering, and Mechanical and Industrial Engineering). This two-year
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machine learning analyses will be performed to determine correlations across stimulation settings and body systems as well as to develop predictive models and biomarkers for physiological and clinical
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presenting at scientific conferences 10% mentoring – training graduate and undergraduate students in the laboratory Qualifications Required Qualifications PhD or equivalent in Biomedical Engineering, Materials
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of stable isotopes, mass spectrometry, and computational modeling to quantify in vivo metabolic fluxes in genetically-engineered mice. Under the direction of Curtis Hughey, the postdoctoral associate will
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-doctoral Associate will develop algorithms and theory for machine learning methods, as well as implement and apply ML methods to problems in domains such as computational biology and neuroscience. This is a