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change accelerate, we urgently need smart, evidence-based tools to plan, manage, and protect our marine ecosystems. At the forefront of this innovation is machine learning. Its ability to process complex
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of quantum sensors for acceleration sensing is a key priority due to its potential to revolutionise inertial navigation, environmental monitoring and geological surveying. Presently, the acceleration sensing
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increasing pressure to reduce its carbon footprint while ensuring long-term performance of infrastructure, particularly in environments that accelerate material degradation. This PhD aims to develop and
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efficient and safer to use in practice. The Research: This project is built around two key questions, giving you the chance to explore both the virtual and physical worlds of robotics: Systematic Exploration
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Deterioration of earthworks slopes (cuttings and embankments), which support transport infrastructure and act as flood defences, is accelerating under increasing weather extremes resulting from
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both sites. The project sits at the interface of cell line engineering, protein science and machine learning and you will receive advanced training in these areas while developing methods to accelerate
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to a net zero economy, electrical power systems are rapidly decarbonizing supply whilst electrifying carbon-intensive demand. There is therefore an acceleration in the connection of low carbon
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Deterioration of earthworks slopes (cuttings and embankments), which support transport infrastructure and act as flood defences, is accelerating under increasing weather extremes resulting from
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infrastructure. However, the increasing application requirements and rising threats from intentional interferences, spoofing, and cyber-physical attacks expose vulnerabilities in conventional GNSS-centric systems
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Are you ready to push the boundaries of engineering innovation and accelerate the world’s transition to carbon-neutral energy systems? Join the Thermofluids Group in the School of Mechanical