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observation for oil spill monitoring You are keen on contributing to new advances in deep learning methodology for earth observation. You will work on deep learning methods for detection of oil spills in
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mathematics, mechanics and statistics. The research is on theory, methods and applications. The areas represented include: fluid mechanics, biomechanics, statistics and data science, computational mathematics
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of the discipline: international politics, public policy and administration, comparative politics, political theory and research methods. The department offers a vibrant academic environment with a good mix of
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interoperability across distributed ecosystems. The SecondPass project addresses these challenges by advancing machine learning (ML)-based and data-intensive methods for the scalable design, processing, and
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. Researchers at Integreat develop theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By combining the mathematical and computational cultures, and the
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duties. How to apply Application letter. Please state your motivation and research interests. A tentative project proposal (5-10 pages). The proposal must include the topic, relevant theory and methods and
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placed at Integreat - Norwegian Centre for Knowledge-driven Machine Learning is a Centre of Excellence, funded by the Research Council of Norway. Researchers at Integreat develop theories, methods, models