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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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provably powerful learning models for graphs will require new mathematical machinery. LOGSMS will combine diverse tools from discrete mathematics, learning theory and machine learning, thus facilitating
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, tasks have a continuous evolution, and the precedence graph becomes dynamic. There is an initial method proposed in the literature, where a static model is proposed, introducing two states of products
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from YouTube. Accept cookie and refresh page to watch video, or click here to open video) About the position We have a vacancy for a PhD position in the Materials Theory group at the Department
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and interdisciplinary data integration develop new AI-based methods, tools, scripts, ontologies and a knowledge graph based on RTG research results and relevant literature provide methodological support
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because explainability is vital in health and medicine. Moreover, it leverages preferring simpler theories over complex ones if both give comparable levels of accuracy. Furthermore, it leverages the power