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are welcome; hiring can only take place once the Master’s/Diploma degree has been completed.) Solid background or strong interest in machine learning, graph theory, and their mathematical foundations. Solid
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the Master’s/Diploma degree has been completed.) Solid background or strong interest in machine learning, graph theory, and their mathematical foundations. Solid programming skills Experience with
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background or strong interest in machine learning, graph theory, and their mathematical foundations. Solid programming skills Experience with machine learning libraries or willingness to acquire Excellent
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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with multitarget estimation for direction-of-arrival (DOA) detection and tracking in radar theory [12]. Graphs are a powerful data structure to represent relational data and are widely used to describe
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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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specialization. Requirements We are looking for candidates who have: Solid analytical and mathematical abilities Experience with machine learning Experience with formal language theory or automata theory Strong
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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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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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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