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candidate for this position holds an MA/MSc-degree or equivalent with a focus on Linguistics, and above-average qualifications, has strong familiarity with formal semantics and pragmatics, and excellent
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providing a structured, semantic framework that enhances knowledge sharing and data reuse across different platforms and systems. Project Aim This PhD will develop an ontology-based methodology to improve
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, or willingness to work with them Experience with multi-modal machine learning methods Familiarity with formal linguistics, particularly formal semantics and pragmatics We encourage applications from individuals
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one-fits-all model was proven unsuccessful. Large Language Models (LLMs) and knowledge graph models are expected to harmonize the formats and semantics but there are many open questions about their
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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
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
intelligent methods that integrate large language models (LLMs) and knowledge graphs to interpret technical documentation and structure complex engineering knowledge. The goal is to create digital twins
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
placement with Rolls-Royce. The research focuses on AI-driven digital twins, using large language models and knowledge graphs for predictive maintenance in aerospace systems. Aerospace systems generate vast
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Coexistence, Tactile Internet, Earth Observation, and Autonomous Transportation. As far as technical enablers are concerned, we leverage expertise on advanced technologies including semantic/task-oriented data
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. Probabilistic and differentiable algorithms for machine learning; 2. Programming language implementation for high performance computing; 3. Programming language semantics and foundations. Your focus will be