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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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This PhD opportunity at Cranfield University invites candidates to pioneer research in embedding AI into electronic hardware to enhance security and trustworthiness in safety-critical systems
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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the implications for urban decision-making and resilience. The PhD researcher will have flexibility in the design and implementation of the project, adjusting the focus based on their interests and the latest
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spaces, before investigating the implications for urban decision-making and resilience. The PhD researcher will have flexibility in the design and implementation of the project, adjusting the focus based
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training will be provided. We particularly welcome applicants who are excited about integrating ecological understanding with data-driven methods. There is flexibility to tailor the research to your
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realistic control laws to virtually “stiffen” highly flexible components and ensure their energy harvesting capabilities. Fully funded by the EPSRC Doctoral Landscape Award (including fees and £22,000 p/a
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. Cranfield University is a world-leading postgraduate institution renowned for its applied research and deep industry connections, particularly in aerospace, defence, and security. Its Centre for Electronic
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. There is flexibility to tailor the research to your strengths and interests. Funding This fully funded Connected Waters Leverhulme Doctoral programme studentship is sponsored by the Leverhulme Trust and
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, and flexible working arrangements ideal for computational and field-integrated PhD research. Methodology You will develop a process-based, spatially explicit population model for European amphibians