363 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" PhD positions in United Kingdom
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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 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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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 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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The Physics and Astronomy Department is located in the heart of the historical area of Bloomsbury. Scientific research and study has been a strong feature of UCL since its inception in 1826 and the
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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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potentially druggable targets. Depending on interest, the student will have an opportunity to contribute to other projects within the team and learn a range of important techniques such as cellular, animal
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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