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Field
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of the challenges is fault detection and diagnosis of bearings subject to low (rotational) speed. As vibration/acoustic signals generated by the faults of low-speed bearings are very weak and often covered by strong
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The primary objective of this project is to establish the evidence base on professional cycling road ‘racing’ trends and the critical tactical moments that determine how races are won. This evidence
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Bayesian inference framework for identifying complex aerospace systems combining with limited experimental data. It can be also used to quantify uncertainties from experimental testing, significantly
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Research Objectives (OBJs) include OBJ1- Modelling framework design, development, and validation of models for the eVTOL and its sub-systems for both control development and evaluation. OBJ2- Design 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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environments—such as fleets with multiple aircraft types. Objectives Objective 1: Map current data types, structures, and interoperability challenges to build a detailed "as-is" understanding of current
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of the project. The student is expected to travel between the University and the Hospital Trust in Liverpool. Project aims and objectives Using the state-of-the-art integrative physiology techniques, this multi
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system using deep learning (DL). The project’s objectives include generating training data from synthetic datasets and real-world images (cadaver and actual intraoperative THR images), developing a marker
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research centre is in Ellesmere Port with the focus on new product discovery for all of its Business Units with a growing emphasis on electrification and biotechnology applications. Key Objectives Synthesize
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railway earthworks. Additionally, the project will integrate environmental data through data fusion and develop automated machine learning tools for anomaly detection and risk assessment. The effectiveness