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records commensurate with grade Desirable criteria Ability to carry out statistical analysis of genetic data Up to date knowledge of machine learning methods applied to clinical and omics data Experience in
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24 Aug 2026 Job Information Organisation/Company Johannes Kepler University Department Institute for Machine Learning Research Field Computer science Researcher Profile Recognised Researcher (R2
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multidisciplinary environments Curiosity-driven and self-motivated working attitude Knowledge of biomechanical modeling, anatomy, vision-based motion capture, machine learning, control systems Keep in mind
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lit. b (postdoc) Limited contract until: Job ID: 6036 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique balance of freedom and support. Join us if
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statistical and mechanistic mathematical modeling, causal inference, and machine learning, applied to longitudinal multi-omics data from pediatric cohorts spanning diverse socio-economic and geographical
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studying how wireless sensing and AI interact in real systems. The work will be carried out in close collaboration with researchers in wireless communications, sensing, machine learning, and robotics, with
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equivalent foreign degree, obtained within the last three years prior to the application deadline Experience with simulation frameworks, system-level performance evaluation, or machine learning, is highly
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department credit cards, review and analyze fund balances; manage ad-hoc hiring of graduate externs, postdocs, adjuncts, and contractors; process payroll for student workers, and enforce operating policy and
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
or equivalent experience in machine learning or a related quantitative field (Computer Science, Artificial Intelligence, Statistics, Mathematics, Physics, Computational Biology/Chemistry). Candidates will be
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strategies within it. You will combine archaeological and landscape data (maps, satellite imagery, archaeological datasets) with machine learning approaches to build a system that highlights promising