45 assistant-professor-computer-science-and-data-"St"-"St" PhD positions at Cranfield University
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attention on food safety, climate-resilient agriculture, and regulatory controls, accurate detection and risk assessment of such mycotoxins have become critical components of modern food science, toxicology
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, finance, and healthcare, where data integrity and system reliability are non-negotiable. This PhD project addresses the integration of robust security measures within AI-enabled electronic systems
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honours degree in materials science, physics, engineering, or a related discipline. The ideal candidate will be self-motivated, with an interest in both computational modelling and practical manufacturing
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engineering or a relevant area. An MSc degree and/or experience and good knowledge in gas turbine theory, thermodynamics, Machine Learning, and computer programming will be an advantage. Funding Sponsored by
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. The integration of AI into hardware not only enhances performance but also reduces energy consumption, addressing the growing demand for sustainable and efficient computing solutions. This PhD project delves
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doctoral training programme dedicated to academic research in space propulsion. R2T2 PhD programmes are already underway at nine UK universities, and the programme overall is centred on the Westcott facility
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-based solutions (NbS) for water and wastewater treatment. The research will explore sustainable engineering strategies to boost their performance to deliver benefits for the environment and society. The
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in radiation–matter interactions, computational modelling, and materials science, with a strong publication record (h-index 36, i10-index 69). Dr Francesco Fanicchia, Research Area Lead: Material
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mitigating jamming and spoofing threats in real-time. Integration of Trusted Execution Environments (TEEs): Investigate the use of TEEs to create secure zones within embedded systems, facilitating secure data
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the computational inefficiencies of physics-based models and enabling faster, potentially more accurate predictions. However, AI models require substantial volumes of high-quality, labelled training data, which