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Discipline: Engineering & Technology, Materials Science, Mechanical Engineering Qualification: Doctor of Philosophy in Engineering (PhD) This project is a collaborative research effort between
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railway earthworks. Additionally, the project will integrate environmental data through data fusion and develop automated machine learning tools for anomaly detection and risk assessment. The effectiveness
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postgraduate institution renowned for its research impact, strong industry partnerships, and focus on applied science. This project is supported by Barilla, a multinational food company dedicated to enhancing
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chemistry or analytical chemistry/chemical engineering will be an advantage. The successful applicant would be expected to spend part of the PhD period based in Bristol at the Airbus site and will receive
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providing a structured, semantic framework that enhances knowledge sharing and data reuse across different platforms and systems. Project Aim This PhD will develop an ontology-based methodology to improve
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partnership with business, professions, the public services, the third sector and other research and learning providers. Applications are invited from potential doctoral students for the following social
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The PhD is funded by Kentown Support, a charity focused on improving community care for children with life-limiting conditions. The PhD will start in October 2025 and is funded for 3 years. Award
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Vapour fed electrolysers can generate green hydrogen even from impure water sources. However, water vapour itself contains gaseous impurities. During this 3-year PhD, we will design and characterise
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. The successful candidate will develop advanced skills in multi-modal sensor fusion, signal processing, machine learning, and integrity assessment, as well as transferable abilities in critical thinking, project
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may also explore embedding these new computational methods into optimisation and machine learning contexts. The new computational techniques developed will be geared towards the following key