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Field
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, structural equation modelling, visualisation, preferably in R Competences in quantitative research methods – ideally knowledge of several of the following aspects of quantitative data analysis: experimental
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for the collection of data to develop and validate prognostic models for filter degradation. Integrated Drive Generator (IDG) Rig: Simulates the operation of an aircraft's IDG, used to investigate fault detection
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this fundamental challenge, the PhD candidate will be part of a wider team to establish methodological framework, combing utilisation of controlled tree growth test, thermodynamic modelling and advanced optical
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for the collection of data to develop and validate prognostic models for filter degradation. Integrated Drive Generator (IDG) Rig: Simulates the operation of an aircraft's IDG, used to investigate fault detection
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such as SHACL (Shapes Constraint Language), the NCT3TA reference model, and standards like HISO 100xx and ISO/IEC 11179-xx. Prior experience with these tools is not required—training will be provided
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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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models. This theoretical project will facilitate close collaboration with experimental groups and enable benchmarking of theoretical predictions. The PhD researcher will be part of the Correlated Quantum
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prognostic models for filter degradation. Integrated Drive Generator (IDG) Rig: Simulates the operation of an aircraft's IDG, used to investigate fault detection, diagnostics, and prognostics in power
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the solidarity and cooperation within and between democratic countries. There have been concerns about the use of Generative AI and Large Language Models (LLMs) as tools for the dissemination of disinformation and
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processes associated with CIN [1], leveraging single-cell DNA sequencing understand CIN heterogeneity [2], and development and implementation of machine learning and AI models to imaging data [3]. The student