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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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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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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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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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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