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clinical data to better characterize disease processes. ● Clinical and multi-omic data fusion: Build machine learning pipelines that integrate electronic medical record data, genomics (animal and microbial
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Chekouo and his collaborators within and outside the University of Minnesota. The research will focus on the development of Bayesian statistical/machine learning methods for the data integration analysis
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willing to live in Minnesota Preferred Qualifications • Coordinated human research studies Physical & Environmental Requirements • Long periods at a computer terminal About the Department The Hormel
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for soft materials, with particular emphasis on thermo–visco–hyperelastic behavior, integrating continuum mechanics, scientific machine learning (SciML), and computational physics. The project aims
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the ability to quickly learn new things and work independently, along with previous research experience in at least one of the following areas: 1) statistical genetics/genomics/omics, or 2) deep/machine
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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
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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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development, data management, and preparation of scientific reports (20%) Computer knowledge to enter data from experiments into existing databases; spreadsheets and web-based applications. Conduct background
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• Skilled in single-cell/population data analysis (e.g., GLMs, decoding) Preferred Qualifications • Background in machine learning or computational modeling (Bayesian methods, neural networks, etc