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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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to automate, accelerate and improve decision making. Analysing graph data requires solving problems at the boundaries of machine learning, graph theory, and algorithmics. Your future tasks: You actively
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. Analysing graph data requires solving problems at the boundaries of machine learning, graph theory, and algorithmics. Your future tasks: You actively participate in research, teaching & administration, which
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to automate, accelerate and improve decision making. Analysing graph data requires solving problems at the boundaries of machine learning, graph theory, and algorithmics. Your future tasks: You actively
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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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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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, 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