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care. While these tools promise to augment medical expertise, their integration into clinical practice poses fundamental challenges that relate to differences between expert knowledge and algorithmic
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infrastructure, affordable computational resources, and advanced modeling algorithms. MPCs excel in handling constrained optimizations and new operational conditions, whereas RLs offer computational efficiency and
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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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mechanism. Recent developments in protein structure prediction and protein de novo design have opened new possibilities for probing such mechanisms. The project will seek to use existing algorithms to new
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analyzing complex biological data, developing innovative algorithms, and contributing to groundbreaking research projects in the department. You will contribute to strengthening our capabilities in the fields
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of machine-learning algorithms for unmanned aerial vehicles; dissemination of the results in international conferences and journals; proposal writing for external funds. Your profile The successful candidate
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implementing inversion algorithms, including a focus on the integration of spatially constrained regularization schemes. Collaborating with forward modeling experts to ensure seamless integration with a recently
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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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across the value chain. Using Bayesian Optimization / Modern Design of Experiment, we build the data-foundation to enable true hybrid development between humans and advanced learning algorithms such as
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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