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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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SQL databases and file repositories. We are now taking the next strategic step: developing ontologies and a dynamic knowledge graph to semantically link our internal data systems - and connect them
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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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, turning geodata into new answer maps. We use knowledge graphs to model these transformations and apply AI methods to scale them across large map repositories, enabling users to explore many ways maps can be
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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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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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). The candidate should have hands-on experience developing state-of-the-art machine learning models, particularly deep neural networks (experience with graph neural networks is highly valued). Their background
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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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) 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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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