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to understand, predict, and treat diseases. You will work with multimodal biomedical datasets including omics, imaging, and patient data and apply cutting-edge AI models such as graph neural networks, transformer
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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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methods to make them usable for transparent energy systems analyses. The collected data will be processed and semantically enriched using methods you develop before being transferred to a knowledge graph
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at the Faculty of Mathematics at TUD. Tasks: generation of hyper uniform patterns (point, scalar and vector fields) application of topological data analysis tools such as persistent homology and graph statistics
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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