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structure would enable you to understand science better at atomic level. You will learn the skills of presenting the results to small and large groups of people via presentations in conferences and meetings
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the challenge of forever chemicals in drinking water. The aim of this research is to develop a smart data predictive model that will support utilities’ evidence-based decision-making to improve the resilience and
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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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systems safer, more efficient, and more sustainable. The aim of this project is to design a smart cognitive navigation framework that information from various sensors and learn to make decisions on its own
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postharvest drying energy demand. Combining applied mycology, food safety modelling, precision agriculture and Net Zero energy systems, the research will deliver energy-efficient, data-driven grain storage
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usability and accuracy, as well as conducting field tests to validate their effectiveness. Additionally, the research will explore the economic viability of these sensors to enhance real-time data collection
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may be required to obtain UK Security Clearance. Funding This studentship is open to UK applicants only. How to apply For further information please contact: Name: Prof. David MacManus Email
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due to a lack of resource. With some water-hungry sectors (such as data centres and other high-tech industries) prioritised for significant growth in water stressed regions, these challenges are set to
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provide a bursary of £20,780 (tax free) plus fees* for three years. This opportunity is open to Home and Overseas fee status students. For further information: Name: Dr Abhijeet Ghadge Email
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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