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, including teeth grinding and normal everyday movements, ensuring the accuracy and reliability of the collected data. Developing, training, and validating state-of-the-art machine learning algorithms
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project offers a unique opportunity to apply cutting-edge computational methods to a fundamental challenge in reproductive biology: understanding why human embryos fail. As delayed childbearing becomes more
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on semi-automated segmentation, classical registration techniques for motion correction, and iterative algorithms for parameter mapping – leading to computation times of many hours or even days per case
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the application criteria in the application statement when you apply. Criteria Essential or desirable Stage(s) assessed at A PhD (or equivalent experience) in Engineering, Maths, Computer Science or another area
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Computational imaging: seeing beyond the capabilities of conventional optics School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Andrew Maiden Application Deadline
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Physics based machine learning algorithm to assess the onset of amplitude modulation in wind turbine noise (with TNEI Group)
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. To fill in this gap, in collaboration with industrial partners, the research will develop novel Machine Learning and Computer Vision methods for detecting and localising. These will be used to develop
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confidentiality. Why does this matter now? Wearable devices have made round-the-clock health data commonplace, cloud computing places vast processing power at our fingertips, and patients are demanding care
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processing, data analysis, data-driven modelling, optimisation and computation algorithms, machine learning models and neural network structures, as well as strong skills and experiences in computational
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Improving Deep Reinforcement Learning through Interactive Human Feedback School of Computer Science PhD Research Project Directly Funded Students Worldwide Dr Bei Peng, Dr Robert Loftin Application