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biased, outdated, or sensitive data?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 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 package funded by
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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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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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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