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. This research direction requires advancements in modern probabilistic tools, including spatial random graphs, random walks, and Markov chains. The position is hosted in the Leibniz Junior Research Group
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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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related field Sound knowledge in the field of artificial intelligence and machine learning Ideally experience with knowledge graphs, semantic search, graph neural networks (GNNs), explainability
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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