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with intelligent technologies. These agents will enable the creation of dynamic, evolving services across various sectors, including healthcare, urban intelligence, and education, fostering continuous
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your PhD journey in the heart of London at the newly established City St George's, University of London, a dynamic institution formed from the merger of City, University of London and St George's
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students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both
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filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
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, outperforming conventional static, dynamic and AI-based techniques. Specifically, in case of any inconsistency, the tool produces a counter example with values that constitute the vulnerability. Such pressure
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of London, a dynamic institution formed from the merger of City, University of London and St George's, University of London in August 2024. As a PhD candidate, you'll become an integral part of the School
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synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project
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dynamic School of Biological Sciences (https://www.southampton.ac.uk/about/faculties-schools-departments/school-of-biological-sciences ). The project will involve close collaboration with an international
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capture technologies. In this project, you will: Develop a 3D Digital Model: Create an advanced computational model of high-pressure mechanical seals. Apply Computational Fluid Dynamics (CFD): Simulate gas
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hydrodynamics for novel marine vehicles, including large ships and small AUVs and offshore renewable energy systems including offshore wind. You are expected to perform advanced computational fluid dynamics