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Hardware Security and Embedded Resilience – Explore AI-powered hardware security primitives, anomaly detection, and self-healing mechanisms for IoT, cyber-physical systems, and mission-critical applications
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development for real-time detection and characterisation of bioaerosols in contrasting environments. The PhD scholar will be advancing scientific knowledge and the evidence base on the fluorescence
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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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. 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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increasingly vital in the aviation sector, enabling the detection of hidden defects without compromising structural integrity, thus ensuring passenger safety and operational efficiency. The project aims
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through advanced modelling and simulation. A key objective is to validate and optimize poroelastic finite element models of brain tissue, making them more accurate and clinically relevant. Additionally
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, or problem. Clearly state the research aim and three or four supporting objectives. Establish the relevance and value of the proposed research question, highlighting its originality and significance. Outline
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This PhD at Cranfield University explores the development of resilient, AI-enabled electronic systems capable of detecting faults and autonomously recovering from failures in real time. The project
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to arrange the tuition fees and living expenses. Find out more about fees here . Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and professional study
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compliance and operational integrity. The application of AI in these areas enhances the ability to predict system behaviours, detect anomalies, and streamline certification workflows. AI-driven tools can