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scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only. Fully-supervised AI techniques have shown remarkable success in
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dynamics and tissue morphogenesis during embryo development using cellular, molecular and mechanical approaches. Cell movements underlie tissue patterns and shapes. Using chick embryos as the model system
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model predictive control (MPC) methods to enable large groups of buildings to dynamically form coalitions and provide flexible energy services. Your work will incorporate advanced robust MPC techniques
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, ensuring stable operation even as system dynamics evolve. Recent advances in Modular Multilevel Converter (MMC) topologies, along with developments in battery and supercapacitor technologies, create new
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Fully-funded PhD Studentship: Adaptive Mesh Refinement for More Efficient Predictions of Wall Boiling Bubble Dynamics This exciting opportunity is based within the Fluids and Thermal Engineering
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The primary objective of this project is to establish the evidence base on professional cycling road ‘racing’ trends and the critical tactical moments that determine how races are won. This evidence
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accuracy is still limited. In contrast, computational fluid dynamics (CFD) models can capture the arc physics and molten pool dynamics, including arc energy transfer and liquid metal convection within
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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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science and interest in fluid dynamics. Prior knowledge about viscoelastic flows and/or porous media is beneficial but not required. Applicants should have, or expect to achieve, at least a 2.1 honours
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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available