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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
of Munich (TUM), Campus Heilbronn. We are looking for exceptional candidates who are interested in pursuing a PhD in either theoretical computer science or graph and network visualization. We seek PhD
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on the following tasks with either with a stronger model-development or application focus: Design knowledge-graph-augmented transformers and retrieval-augmented generation (RAG) pipelines that enable
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application focus: Design knowledge-graph-augmented transformers and retrieval-augmented generation (RAG) pipelines that enable semantic querying and reasoning over materials-science/physics corpora Developing
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Dortmund, we invite applications for a PhD Candidate (m/f/d): Multidimensional Omics Data Analysis You will be responsible for Setup a knowledge graph in neo4J for microbiome research Integration
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starting date is November 2025. The topic of the PhD project will be theoretical research in discrete optimization, with a particular focus on either graph algorithms or multiobjective optimization
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) Research area: Large Language Models (LLMs), knowledge graphs (KGs), commonsense knowledge Tasks: foundational or applied research in at least one of the following areas: LLMs, KGs, knowledge extraction
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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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. 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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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