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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our
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supervised by Sebastian Throm. The subject area of the announced position covers kinetic theory, non-local diffusion and dynamics on graphs. The precise research direction will be determined together
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to effectively compile linear algebra expressions when the matrix sizes are unknown at compile-time. The project aims to address the problem using e-graphs. An e-graph is a data structure commonly used in
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). By combining expertise in metabolomics, multi-omics, knowledge graphs, and artificial intelligence, this project fosters an essential interdisciplinary synergy to address the challenges of integrative
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mechanisms of online media platforms by granting users full control over their sensitive information. Innovation. To meet users' varying privacy demands, this project put forward a graph-based user
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the context of algorithmic problems related to constraint satisfaction and graph homomorphism and isomorphism problems. It brings to bear significant new mathematical (algebraic and topological) methods
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the context of algorithmic problems related to constraint satisfaction and graph homomorphism and isomorphism problems. It brings to bear significant new mathematical (algebraic and topological) methods
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open-source tools and training modules for global utility adoption. The framework combines physics-informed graph-neural-networks (GNNs), diffusion model, and explainable reinforcement learning (XRL
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covers kinetic theory, non-local diffusion and dynamics on graphs. The precise research direction will be determined together with the successful candidate upon personal background and interests