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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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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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- from developing scalable quantum processor technologies to solutions for the quantum-classical control and readout interface, and all the way to quantum algorithms and applications. The long-term
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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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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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biased, outdated, or sensitive data?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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, monocular depth estimation, and reinforcement learning. You will develop innovative algorithms that empower aerial robots to actively explore unknown, unstructured, and GNSS-denied environments while