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Vacancies 2x PhD positions in the Mathematical Foundations of Machine Learning on Graphs and Networks Key takeaways The Discrete Mathematics and Mathematical Programming (DMMP) group
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. This project focuses on fundamental algorithmic questions on geometric networks and, in particular, on geometric intersection graphs: graphs whose nodes correspond to disks or other objects in the plane and that
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of empowering citizens for the use of such technology. This work will be developed with the support of the interplay of Semantic Technologies (e.g., ontologies, knowledge graphs) and Artificial Intelligence
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explainable AI (XAI) methods with user-centred interaction design, combine machine learning with alternative AI methodologies (e.g., rule-based reasoning, knowledge graphs, hybrid approaches where relevant
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of such technology. This work will be developed with the support of the interplay of Semantic Technologies (e.g., ontologies, knowledge graphs) and Artificial Intelligence. Moreover, domain explanation requirements
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of such technology. This work will be developed with the support of the interplay of Semantic Technologies (e.g., ontologies, knowledge graphs) and Artificial Intelligence. Moreover, domain explanation requirements
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Optimization: Mathematical Phylogenetics During this project, you will work on fundamental graph-theoretic and algorithmic problems in mathematical phylogenetics. Job description The Discrete Mathematics and
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programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. In particular, you will be part of the Causality team under the supervision
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from reactive to proactive. The goal is to increase transparency and trust in the DNS namespace. Key research activities will include applying machine learning and graph-based techniques to uncover
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. Methodological Approach Candidates will develop and apply state-of-the-art machine learning techniques, including deep learning, representation learning, variational autoencoders, and graph-based models. A strong