121 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" positions in Norway
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- University of Oslo
- NTNU Norwegian University of Science and Technology
- NTNU - Norwegian University of Science and Technology
- UiT The Arctic University of Norway
- University of Bergen
- University of Stavanger
- Western Norway University of Applied Sciences
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- NORCE Norwegian Research Centre
- Norwegian University of Life Sciences (NMBU)
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- OsloMet – Oslo Metropolitan University
- Peace Research Institute, Oslo (PRIO)
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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, physics, and related fields, including early-stage researchers eager to contribute to this emerging scientific frontier. Duties of the position Fundamental contributions in embodied AI
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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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. We welcome motivated applicants in robotics, control, AI, machine learning, physics, and related fields, including early-stage researchers eager to contribute to this emerging scientific frontier
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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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to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field
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centered around a unique, open-source digital platform enriched with data and powered by domain knowledge-based advanced machine learning and artificial intelligence capabilities. By introducing a Digital
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, currents, water levels, wind, and ice. Machine learning models will be developed to forecast future variations in such dynamic conditions and to incorporate the operational state of the vessel into routing
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measurements are most informative and guiding where, when and how to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more