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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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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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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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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