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to the perception of animacy when interacting with objects and people using videos (behaviour, fMRI, and MEG), 3) determine to what extent these brain representations and dimensions change when humans are immersed in
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– such as tandem neural networks , video diffusion models , and reinforcement learning – will be explored to efficiently navigate these high-dimensional, nonlinear design spaces. To achieve robust property
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compressible gas dynamics, heat transfer, free-surface/melt behaviour, and mass transfer driven by phase change, within a GPU-accelerated solver to reduce simulation turnaround times. You will develop and
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compressible gas dynamics, heat transfer, free-surface/melt behaviour, and mass transfer driven by phase change, within a GPU-accelerated solver to reduce simulation turnaround times. You will develop and
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on large annotated datasets. Memory-efficient deep learning: Model compression, pruning, quantisation, selective memory replay, and efficient training strategies. Energy-efficient deep learning: Methods
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Assessment Systems: Toward Trustworthy AI for Complex Educational Evaluation Image and Video Analysis Using Machine Learning Algorithms Mathematical and Computational Neuroscience, from neural data and network
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from video recordings. This studentship would be part-sponsored by an industrial partner, Neurotherapeutics Ltd. Use translational neuroimaging and neurophysiology methods together in a rodent model of
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the education/instructional videos contained within them. Therefore, you will be contributing to the development and refinement of a potential medical device that could become adopted into clinical
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of computer science and engineering. Research areas incorporate digital signal and image processing, sensor network, Internet of Things, healthcare as application area, multimedia, image and video processing, cyber
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This is an exciting PhD opportunity to develop innovative AI and computer vision tools to automate the identification and monitoring of UK pollinators from images and videos. Working at