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the exploration of viable opportunities for the application of immersive learning environments to address key performance questions in sport. The successful candidate will work with a range of existing and new high
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to develop new deep learning methods for audio-visual scene analysis in a smart home environment. This will involve the use of heterogeneous sensors, e.g. microphones and cameras, for analysing various sources
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at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students
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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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This project has a start date of October 2024. There is a growing body of research describing the benefits of exposure to natural environments for human physiology and psychology. Nature, therefore
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between these processes is not well understood because Alfven waves can only be investigated at laboratory scale in a very high magnetic field environment. As a consequence, experimental data on Alfvenic
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dynamics associated with both rigid and flexible structures commonly found in various devices that harvest energy from the environment. These structures include wave energy devices, offshore wind turbine
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joining this ambitious effort, you will contribute to improve the natural environment, the state of our rivers, and help shape future funding and policy strategies in the UK and abroad. Water infrastructure
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Supervisory Team: Dr Erisa Karafili, Dr Basel Halak PhD Supervisor: Erisa Karafili Project description: Cyber-attacks in multi-domain environments present a critical challenge due to
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research; you will be positively impacting people’s lives and the environment. Eligibility Candidates must hold a UK Bachelor degree with a minimum of Upper Second Class honours in Computer Science