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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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theory and probabilistic methods, and it hopes to clarify how these two settings, hypergraphs and posets, connect through their shared saturation questions. Combinatorics and mathematics in Umeå
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theory and probabilistic methods, and it hopes to clarify how these two settings, hypergraphs and posets, connect through their shared saturation questions. Combinatorics and mathematics in Umeå
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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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transformation over the time. Taking this research further may mean considering the dynamics of the precedence graph, with a more general mathematical formulation and dedicated exact or heuristic methods. It may
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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