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semantic querying and reasoning over materials-science/physics corpora Developing pipelines for semantic enrichment of unstructured data, including entity recognition, relation extraction, and automatic
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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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12.10.2021, Wissenschaftliches Personal The TUM Professorship for Data Science in Earth Obervation is seeking a full-time PhD candidate on the topic of “Multi-scale Semantic Understanding
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hydrodynamic models and 3D urban semantic models in collaboration with project partners, with applications to recent major flood events in Germany, Brazil, and Spain. Supervision & collaborations: The PhD will
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toward generative and multimodal AI methods that connect simulation, perception, and control within large-scale digital twins of urban traffic systems in Munich. The focus lies on advancing semantic
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literature Design and test tools to identify and link key entities (e.g. species names, people, places), including disambiguation and semantic enrichment tailored to biodiversity and entomological contexts
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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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integration of energy systems data and models and apply data science methods to make them usable for transparent energy systems analyses. The collected data will be processed and semantically enriched using
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? What are the elements of meaning (image ciphers) that make up the semantic field of images? To what extent can images be precisely determined in their semantic content? Such questions need to be explored
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, Google.org, SAP, Merck, TUM Klinikum, Holtzbrinck). Diverse research topics and technologies, including: Conversational Semantic Search, Question Answering Systems, Complex Information Extraction. Fact