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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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English. A strong background in graph theory and graph algorithms is necessary. For PhD position 1, we appreciate prior mathematical exposure to at least one of the following topics: random graphs
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successful applicants will take part in the research and teaching activities at the Chair of Algorithmic and Structural Graph Theory within the Institute of Theoretical Computer Science at TUD. The main
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department is available at: https://www.umu.se/en/department-of-computing-science/ Project description Graph transformation is a well-established theory that studies computational methods to transform graphs
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to the success of the whole institution. The Faculty of Computer Science, Institute of Theoretical Computer Science, thenewly established Chair of Algorithmic and Structural Graph Theory offers a position as
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the department is available at: https://www.umu.se/en/department-of-computing-science/ Project description Graph transformation is a well-established theory that studies computational methods
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ability to work both independently and collaboratively are essential. A solid background in combinatorics, especially graph theory, together with basic knowledge of probability theory, are required
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of extensive-form games. These studies highlighted the effectiveness of combining tools from graph theory, mathematical programming, and dynamic programming to compute pure Nash equilibria in extensive-form
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including: * Algorithmic game theory * Approximation algorithms * Automata and formal languages * Combinatorics and graph algorithms * Computational complexity * Logic and games * Online and dynamic