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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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-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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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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-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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environmental benefit, and enable smarter investments in water infrastructure and management. By joining this ambitious effort, you will contribute to improve the natural environment, the state of our rivers, and
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experiences, rather than interacting directly with the environment in real-time. Off-line reinforcement learning can be beneficial in scenarios where online exploration is costly, dangerous, or impractical
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. This technical project designs the affordances of an advanced spatial audio system with awareness of the user's intentions, choices and environment in order to adapt the sound reproduction intelligently
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and clinicians: an excellent opportunity for inter-disciplinary training. 3.5 years funding includes stipend, tuition fees for UK students A supportive environment as signatories of the Researcher
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dimensions, fully 3D-printed electronics integrated into structures, and responsive materials tuned to their environment. In this PhD studentship, we will develop the materials, methods, and designs to control