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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
unstructured maintenance records using LLMs, ontologies, and knowledge graphs. Build a standards-aligned semantic framework for interoperability and scalability. Model degradation over time using temporal
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planning -Semantic-based Exploration -Source localization -Perception in sensor-degraded environments: -Localization in smoke and dust filled environments -Scene awareness -Biometric/triage evaluations, etc
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on recent advances in Higher-Order Mathematical Operational Semantics, a pivotal generalization of Turi and Plotkin's seminal approach to structural operational semantics. We seek an apt and motivated PhD
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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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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
a standards-aligned semantic framework to ensure interoperability, reusability, and scalability across systems and sectors •Model system degradation over time by developing temporal knowledge graphs
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. -Machine learning code generation for autonomous translation of payload data semantics. -Dictionary learning and algorithms for translation between major data modeling languages. -Model-based System
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models to identify patterns and structures in the learnt features that correlate with high-level semantic concepts. • Objective 2: Evolution of Learnt Features -- Analyse the hierarchical progression
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. •Detailed semantic understanding of operational environments for Machine Situational Awareness, particularly within contested, congested and degraded scenarios. •Fully autonomous robust intelligence data