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to algorithms with actionable performance guarantees. More specifically, the research will revolve around one of the following streams: Convergence with high probability in stochastic optimization: Going beyond
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. Application deadline: 24 March 2025 at 23:59 hours local Danish time. What we are looking for Hired candidates will join the Algorithms Section and the new SDU Centre for Computer Science and Artificial
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Post doc position in theory of machine learning at Department of Computer Science, Aarhus University
is on understanding and improving the performance of classic learning algorithms, in particular Boosting and Bagging, both in terms of speed and generalization capabilities. The project also allows
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(entities) given the rules and the rules given the molecules. The aim of this project is to develop a theory and accompanying algorithms to decide if an abstract system can be instantiated by a concrete
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well as developing solution algorithms applying mathematical and computational approaches. The group has a particular focus on automated decision making in autonomous cyber-physical systems. Autonomous systems and
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(entities) given the rules and the rules given the molecules. The aim of this project is to develop a theory and accompanying algorithms to decide if an abstract system can be instantiated by a concrete
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for human control of complex robotic systems with high levels of agency and minimal cognitive effort. Short description: This project will develop novel AI algorithms to decode human intention from
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algorithm development in the biomedical context. We are particularly, but not exclusively, interested in candidates with competence in multi-modal data integration, including electronic health records, omics
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technologies such as artificial intelligence (A), big data, and algorithmic decision-making become increasingly embedded in organizational practices, companies face new ethical, societal, and managerial
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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