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modelling tools to understand and tailor the physical and chemical interactions at the interfaces within metascintillators. Cranfield University’s Centre for Materials is internationally recognised
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and kinematic models with machine-learning-based channel state information (CSI) prediction to enable robust, low-latency connectivity across multi-layer NTN systems. This PhD project sits
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targets the development of advanced coatings to prevent cell-to-cell propagation during runaway events. It combines experimental studies, numerical modelling, and real-world burner rig testing, culminating
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of novel AM materials on corrosion response of key component and develop a model to predict their behaviour. To address the goals set for tackling international climate change, the power sector needs
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health management (IVHM) system that leads to enhance safety, reliability, maintainability and readiness. Generally, prognostics models can be broadly categorised into experience-based models, data-driven
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recovery in critical applications, including aerospace, healthcare, and industrial automation. Research Focus Areas: Predictive Analytics for Fault Detection: Develop AI models that predict potential system
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reliability and maintenance strategies. Filter Rig: An experimental setup to study filter clogging phenomena, allowing for the collection of data to develop and validate prognostic models for filter
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. The project delves into areas such as hardware-based security measures, tamper detection, and the integration of explainable AI models within embedded platforms. Situated within the esteemed IVHM Centre and
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This PhD opportunity at Cranfield University explores how next-generation AI models can be embedded within resource-constrained electronic systems to enable intelligent, real-time performance
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. The enhanced image quality will support earlier and more reliable detection of eye diseases. Combining artificial intelligence with mathematical modelling, this non-invasive, cost-effective approach has