177 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" Fellowship positions in Norway
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integrated into creative and artistic subjects to enhance human creativity, learning, and formation. The position allows for research into various ethical considerations related to the use of AI in the arts
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be developed specifying the competencies that the research fellow is expected to acquire. The institution is responsible for ensuring that the career plan is followed up and that the research fellow
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, geometric deep learning. Considered an advantage: experience in programming or course work in computer science, algebra, topology or differential geometry, knowledge of topological data analysis or machine
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for the project: DC4 - The role of invasin-integrin interactions in Klebsiella and Enterobacter infections Supervisor Dirk Linke: https://www.mn.uio.no/ibv/english/people/aca/dirkl/index.html Co-supervisors: Håvard
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equations (PDE). Examples of models in the scope of the project include particle models, stochastic PDE and models from fluid dynamics and machine learning. What skills are important in this role
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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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Norwegian courses. Required selection criteria You must have completed a doctoral degree in (machine learning, statistics, or similar). You must have a professionally relevant background in algorithms
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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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the program’s content and learning outcomes for the PhD program in Fine Arts. It is a prerequisite for employment that the faculty has the supervisory capacity and infrastructure necessary to support
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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