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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
generate vast amounts of operational and maintenance data, much of it remains fragmented and underutilized. Unlocking insights from this unstructured data could enable earlier fault detection, improved
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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detection and classification algorithms using measured and/or simulated data, such as current pulses from cable faults (breakdown), partial discharges and external noise. In addition to being part of
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processing power of a novel photonic integrated circuit architecture [Heuck2023]. This includes studying the effects of optical loss and decoherence and methods to overcome these by error detection and
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
engineers detect faults earlier, track system degradation, and make better-informed maintenance decisions. But how can we turn this complex information into something reliable, explainable, and actionable
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of Twente is looking for a highly motivated and talented PhD candidate to join our team. In this position, you will explore advanced frameworks to make complex interacting programs fault-tolerant and future
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parameters by trial-and-error, leading to a time consuming sub-optimal selection. In the domain of high precision machining, tools are prematurely discarded to avoid the risk of costly non-conformities
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maintenance (O&M) practices for wind turbines, with a focus on fault types that degrade turbine and plant-level power performance. Identifying key signals or performance indicators related to asset health and
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to join a cutting-edge research project at the intersection of microelectronics and quantum computing. As the quantum computing field rapidly advances toward large-scale, fault-tolerant systems, one
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of underwater acoustic environment or low signal-to-noise environment. We will develop more effective deep learning to increase data communication speed and reduce error probabilities. The project aims