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Models, Knowledge Graphs, and related fields (e.g., Graph Machine Learning) Tasks: scientific research in at least one of the following areas: Natural Language Processing, Knowledge Graphs, Machine
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Knowledge Graph. Together, you will work on: Developing and extending the Helmholtz KG data model, translating scientific and infrastructure requirements into robust, maintainable schemas and ontologies
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to make them usable for transparent energy system analyses. You will process and semantically enrich the collected data before it is integrated into a knowledge-graph-based metadata platform. In
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The LIT - Leibniz Institute for Immunotherapy (foundation under civil law) (https://lit.eu/ ) – is a biomedical research centre focusing on translational immunology in the fields of cancer
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yield new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at leveraging graph-theoretic approaches to analyze and predict food-effector
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, learner-aware sequencing of content. This includes work on semantic parsing, structured NLP, graph-based neural models, metacognitive prompting, ontology alignment across disciplines, and human-in-the-loop
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the form of graphs to analyze and predict food-effector systems. Key Responsibilities Develop Probabilistic Machine Learning Models to integrate graphs and food-related omics data Multi-omics integration
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Technologies for the Social Sciences (KTS), Team Information Extraction & Linking located in Cologne, is looking for a Research AssociateKnowledge Graphs (Salary group 13 TV-L, working time up to 100 %, limited
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 3 months ago
new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at leveraging graph-theoretic approaches to analyze and predict food-effector
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molecular level. To yield new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at leveraging graph-theoretic approaches to analyze and