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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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directions will be pursued to enhance column generation using machine learning. The first line of research focuses on improving scalability by using Graph Neural Networks to identify and eliminate non
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biological data, development of deep learning and large language models for biological discovery or graph-based methods for molecular and cellular networks. The technological foundation further consists
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, specifically modelling the complex interrelations among infrastructure, human operators, and organizational structures using dynamic graphs, system dynamics, Agent Based Models, and discrete event simulations
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: algorithmics, graph transformation and algorithm engineering. Exposure to systems chemistry or systems biology is an asset but not a must. Proven competences in programming and ease with formal thinking are a
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. The current research topics include disorder effects on phase transitions (diluted ferromagnets, long-range correlated defects, spin glasses, random graphs and networks), long-range interacting systems
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and polyploid crop species and benchmark them against other methods such as graph-based methods. This project will combine algorithm development and computational programming with large population
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). "Statistical field theory applied to complex networks” "Quantum geometrogenesis – Graph theoretic approaches to building spacetime” web page For further details or to discuss alternative project arrangements