412 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" scholarships in United Kingdom
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network integration for emerging low-energy opto-electronic AI systems and beyond. The challenge: Machine learning and neural networks are super-charging the complexity of problems that computer algorithms
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language processing, statistical machine learning and causal inference. The scholarship will fund course fees up to the value of home fees*, a tax-free stipend of no less than £20,780 per annum), plus additional
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bespoke methods – including advanced data modelling approaches (e.g., machine learning, digital twin models) and AI techniques where appropriate – to provide novel solutions that enable sports to make
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machine learning and knowledge organisation, for exploring the significant moving image collection at BT Group Archives. This project will be jointly supervised by James Elder and Elspeth Millar( BTGA
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following: Strong programming skills (preferably in Python), with experience in machine learning/AI or software engineering for interactive systems Desirable experience in game development or design using
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machine learning (ML) and artificial intelligence (AI) workflows, the project aims to create a comprehensive molecular atlas and identify novel, translational biomarkers and therapeutic targets. Project
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geolocated social media data, and computational techniques from network science and machine learning. It is interdisciplinary, combining theories of healthy and accessible cities with computational data
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: machine/deep learning, numerical modelling, statistics, optimisation, scientific computing • Ability to work across disciplines and collaborate with academic and industrial teams Desirable: • Experience in
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foundation in either machine learning or mathematical/computational neuroscience, demonstrable programming experience (Python/PyTorch), and the curiosity to work across disciplinary boundaries. A background in
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: Earth Sciences, Bioscience, Interdisciplinary Life and Environmental Science, Inorganic Materials for Advanced Manufacturing, Chemical Synthesis for a Healthy Planet,Statistics and Statistical Machine