9 machine-learning Postdoctoral positions at University of Minnesota in United States
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landscape constrains or enables discovery. The project draws on tools from topological data analysis (e.g., persistent homology, Euler characteristic curves, discrete curvature), machine learning (e.g
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. Proficiency with common computer software, physiology data analysis programs, and biostatistical software. Preferred Qualifications Experience with preclinical animal subject research principles and practices
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transportation systems and autonomous driving. • Strong understanding of generative AI, deep learning, and multimodal machine learning, with hands-on experience. • Excellent programming skills and proficiency with
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2026 is acceptable. Strong quantitative background with demonstrated proficiency in Matlab, or in another programming language, such as R or Python, with motivation to learn Matlab. Ability to work
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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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• 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