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Overview: As data becomes more accessible, new challenges arise around how best to use it—especially in complex, multi-system environments like aerospace. Ontologies offer a powerful solution by
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agents, and lies at the intersection of social ontology, collective ethics, moral responsibility and the ethics of blame and praise. The aim of the project is first to develop a novel methodology for
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Essential skills: Experience with ontologies and knowledge graphs Experience with data privacy and interest in data governance Familiarity with, or keen interest in, decentralised technology (e.g. SOLID
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(DT) communication protocols for interfacing with different types of systems and data sources. Using the DR research to create guidelines for the development of the ontological structure for the DT
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of systems and data sources. Using the DR research to create guidelines for the development of the ontological structure for the DT. Development of DT information modelling, data fusion, and forecasting
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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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Wikibase instance Curate and model historical migration datasets within the dedicated Wikibase instance Contribute to the design of ontologies and metadata schemas for the knowledge graph Develop data-driven
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for Sustainable Housing and buildings”, you will: Develop an ontology of regenerative building production by analyzing how regeneration affects on-site praxis, economic structures, and business models and
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, comprising business and human rights, collective epistemology, phenomenology, and social ontology. Besides its academic impact, it will inform policy-making, regulation and the public debate. Organisation
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
aerospace environments. The objectives of the PhD are: •Extract structured engineering knowledge from unstructured maintenance data using LLMs, and represent it using ontologies and knowledge graphs •Develop