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to a wide range of engineering problems, including real-time structural health monitoring, vibration analysis, and control design. The ideal candidate will have an outstanding engineering or related
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framework for data‑based structural health monitoring. The Ph.D. project aims to start in the fall of 2025. The Ph.D. position holder will be required to work in Trondheim, together with the Structural
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research infrastructure. Selected candidates will benefit from the ICN2 PhD Programme, which comprises a structured training path with scientific seminars, and technical and transferable skills training
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new standards in computational metabolomics – facilitating biomarker discovery, advancing personalized health monitoring, and improving clinical decision-making. The work will be carried out under close
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mixed research methods—including behavioural surveys, environmental monitoring, and dynamic thermal modelling—the project aims to generate retrofit strategies that improve energy efficiency, reduce carbon
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infrastructure. Selected candidates will benefit from the ICN2 PhD Programme, which comprises a structured training path with scientific seminars, and technical and transferable skills training throughout
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, which are utilized for efficient, non-destructive inspection, damage characterization, damage evaluation, and health monitoring of wind turbine structural elements and components. We develop tools and
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
and reasoning techniques to support predictive maintenance and asset health monitoring •Design feedback mechanisms that deliver interpretable insights (e.g. alerts, recommendations, confidence scores