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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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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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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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Carlo (MC), as well as quantum approaches like Density Functional Theory (DFT). Develop novel Graph Neural Network (GNN) potentials to accurately represent the catalytic behavior of specific species
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, graph theory, satisfiability problems, discrete optimization. Strong interests in chemistry as well as proven competences in programming and ease with formal thinking are a necessity. This PhD project is
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, graph theory, satisfiability problems, discrete optimization. Strong interests in chemistry as well as proven competences in programming and ease with formal thinking are a necessity. This PhD project is
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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
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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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) biological knowledge about GRNs from bioinformatics and system biology, (b) graph theory and topological data analysis for network modeling from mathematics, and (c) robust machine learning (ML) and GenAI from