288 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" positions at University of Oslo
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pressure-build ups in potential multi-site storage licenses. The research will help to suggest best practices for machine learning integration in de-risking CO2 storage sites. We seek a candidate with a
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Machine Learning. We are looking for a motivated candidate, who has interest in both theoretical, methodological and applied research in anomaly detection in sequential data settings, and who is excited
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optimization. Experience with quality-diversity methods is a plus. • Experience with machine learning and artificial intelligence. • Strong programming skills (e.g., Python, C++), and familiarity with ROS
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-working candidate. Main responsibilities Develop and apply machine learning and statistical modeling techniques, including novel AI architectures, for the analysis of complex traits and precision prediction
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find more information about the research group here: https://www.med.uio.no/klinmed/forskning/grupper/kardiovaskular-crg/ The PhD candidate will be funded by the K. G. Jebsen Centre for Cardiac
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exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8 Grade requirements: The norm is as follows: The average grade point for courses included in
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the final exam. Desired qualifications: Experience in areas such as machine learning, computer vision, control sys-tems, perception, control engineering, or autonomous systems Familiarity with ROS (Robot
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the research fields of cardiovascular biomarker research and cardio-oncology. You can find more information about the research group here: https://www.med.uio.no/klinmed/forskning/grupper/kardiovaskular-crg
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collaborative EU funded doctoral training network HEALENAE: Health and Environment in Africa and Europe. The network will consist of 15 PhDs, that together will work and learn as an international cohort and
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mediators using in vivo mouse models. The position has a duration of two years. The project group is part of a vibrant and inclusive research environment (https://www.ous-research.no/kt/) at the Department