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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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involves the use of quantum chemistry, machine learning, and genetic algorithms to search for new homogeneous chemical catalysts. Who are we looking for? We are looking for candidates within the field
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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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algorithm. Design methods: Develop novel control methods for power electronic converters feeding electric machine Simulation: Learn advanced simulation tools such as Ansys to simulate and analyze the effect
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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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biased, outdated, or sensitive data? That's where the project TRAI comes in. This research project aims to develop machine unlearning algorithms to selectively erase specific knowledge from trained AI
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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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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
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programming Creating their own mechanical designs, implement and test them accordingly, Implementation of control algorithms on physical experiments. In addition, the candidates are expected to contribute with
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learning techniques, as well as methodological issues related to AI (e.g. the fairness and transparency of algorithms) Energy, Environment and Resources Firm Behavior, Innovation and the Functioning