403 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" scholarships in United Kingdom
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recently completed a pilot study investigating the use of atmospheric pressure ionisation mass spectrometry coupled with machine learning for differentiating between brain tumours and normal tissue
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About the Project A fully funded PhD scholarship is available at UCL Electronic & Electrical Engineering (4 years, home tuition fees covered, stipend provided). Exceptional international candidates
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the complex multiscale nonlinear interactions at the origin of such extreme events. In this project, you will develop machine learning-based reduced-order models which can accurately forecast
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the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density
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the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density
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Supervisor: Professor Fernanda Duarte Start date: 1st October 2026 Applications are invited for a fully-funded DPhil studentship in Machine Learning Interatomic Potentials for Metal-Ligand
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). The project investigates how machine learning (ML) can be used to enhance the modelling of boundary layers in industrial CFD simulations, where complex geometries and computational constraints limit near-wall
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About the project: From Brittle to Ductile: Machine Learning 3D Fracture Simulations for Extreme Environments Supervisor: Prof, James Kermode, University of Warwick Develop cutting-edge machine
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A Human-Factors Investigation of Automation, Decision-Support and Machine Learning in Clinical Decision-Making Tasks. This PhD project is based within the Human Factors Research Group in the Faculty
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About the project: Supervisor: Professor Nicholas Hine, University of Warwick This project uses cutting-edge computational and machine learning methods to accelerate catalyst discovery for fuel cell