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of solvers for stochastic optimization problems, and test the methods on real-life data. As part of the PhD you will be following advanced courses to extend your skills, implement and test algorithms, and
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transformative advancements in scientific discovery. Responsibilities As a Senior postdoc, you will be able to contribute to designing your own project focusing on implementing cutting-edge algorithms for graph
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on developing machine learning algorithms to support the use of complex urban simulators in decision-making under uncertainty. This PhD project shifts the focus from optimality to relevance in urban land-use and
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integrate machine learning algorithms and Earth System Models to emulate carbon processes in the ocean connected to the biological activities. You will be enrolled in DTU’s Section for Oceans and Arctic and
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separate job advert “Postdoc in advanced reconstruction algorithms for time-resolved nano CT of sustainable cement ”) Research and teaching efforts at a section and departmental level as appropriate and
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Job Description We invite applications for a fully funded PhD position focused on the development of advanced computer vision and machine learning algorithms for detection and identification
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-transpositions (plastys) and suture practices for surgical procedures. The specific PhD-project aims at developing efficient hyper elastic-based topology optimization algorithms that take into account skin
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of Europe’s ‘green transition’ to clean energy. You will work on cutting-edge research tasks, with objectives including • new algorithms and strategies to improve autonomous Airborne Wind Energy (AWE) operation
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candidates in any field of machine learning theory, algorithms and computation or in AI applications in e.g., the social, neuro, or natural sciences, are encouraged to apply. The position comes with a start
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goals Teach and co-supervise BSc and MSc student projects Participate in Arctic field campaigns We expect you to have: Experience in working with large data sets and development of algorithms. At least