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a doctorate. We are looking for: candidates with a Master’s degree in mathematics or a closely related field and with a strong background in probability theory. Prior knowledge in spatial stochastic
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a doctorate. We are looking for: candidates with a Master’s degree in mathematics or a closely related field and with a strong background in probability theory. Prior knowledge in spatial stochastic
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to model and analyse the intrinsic complexities of these systems. This research direction requires advancements in modern probabilistic tools, including spatial random graphs, random walks, and Markov chains
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Description Join us in seeking exciting new developments using graph theory in nearest neighbor models for active matter! Do you enjoy working with graph theory, and seeing how functions on graphs can inform
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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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Profile: We seek someone with strong mathematical maturity in control theory, dynamical systems, or applied mathematics. Familiarity with nonlinear systems analysis, graph theory, and formal methods (e.g
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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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, 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