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for downstream tasks. In this project, you will develop novel unsupervised machine learning methods to analyse cardiovascular images, primarily focusing on MRI. In your research you will train models to learn a
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: Computational Modelling: Employing simulation tools (e.g., GEANT4, light transport) to explore novel metamaterial designs, predict performance, and optimise key parameters such as timing resolution, light yield
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functional motifs are encoded in HS chains and how they influence their biological activity. Using gastruloids as a model system with which to study GAG structure/function relationships. Generating gastruloids
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cells or the tumor microenvironment reduces tumor growth and extends survival in preclinical models, underscoring their potential as dual-function therapeutic targets. To address this, we aim to develop
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experimentation and finite-element modelling. Research themes would be flexible including green steel formability under the EPSRC ADAP‑EAF programme for automotive and packaging applications; or micromechanical
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, multi-source learning is used to integrate diverse patient populations to build robust models, but having to protect sensitive information. Various modern ML paradigms are proposed to address the diverse
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verification methodology and corresponding toolchain to detect and mitigate such threats to CPS at the design time making the CPS resilient-by-design. Typically, CPS are modelled as hybrid systems, comprising
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to analyse cardiovascular images, primarily focusing on MRI. In your research you will train models to learn a distribution of normal cardiac anatomy and function (including motion) from healthy subjects. By
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response timelines. Building on this foundation, the project will apply scenario modelling and simulation techniques to investigate emergency event propagation, routing strategies, vehicle-task assignment
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Boreal forest recovery after clear-cut: We are looking for a highly motivated PhD student to collect data on and model hydro(geo)logy and greenhouse gas exchanges of boreal forests after