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depletion, toxic algae, and pollutants. This natural sensitivity makes them powerful bio-sensors for environmental monitoring, capable of providing early warnings of ecosystem stress. However, harnessing this
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to optimise built-environment thermodynamics and occupant comfort by creating predictive AI tools for spatiotemporal heat transfer. Machine learning algorithms will identify energy inefficiencies and propose
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of Oxford. The post is funded by United Kingdom Research and Innovation (UKRI) and is for 24 months. The researcher will develop 3D mapping and reconstruction algorithms with relevance to mobile robotics
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across Scotland’s west coast. It will evaluate the practicality of different image capture techniques and the potential of different sensor types (e.g., RGB, multispectral) to generate beach litter images
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Primary Supervisor -Prof Michal Mackiewicz Scientific background Marine litter is a key threat to the oceans health and the livelihoods. Hence, new scalable automated methods to collect and analyse
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Competition Funded Students Worldwide Prof F M Boissonade, Dr Thomas Paterson Application Deadline: 15 January 2026 Details A fully funded PhD opportunity to participate in the world-leading research
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Worldwide Prof Tom Hayward, Dr Matthew Ellis Application Deadline: 15 January 2026 Details A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral
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AI-assisted grading of end-of-life wind turbine composite materials for a circular economy Offshore Wind CDT PhD Research Project Competition Funded UK Students Dr James Maguire, Dr T Rogers, Prof
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Intelligent Thin Film Manufacturing; routes to responsive slot die coating. School of Chemical, Materials and Biological Engineering PhD Research Project Self Funded Prof Jonathan Howse Application
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), Institute of Sensors, Signals and Systems (ISSS), supervised by Prof Mathini Sellathurai (https://www.hw.ac.uk/schools/engineering-physical-sciences/staff-directory/mathini-sellathurai.htm). For further