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determine the impact of community acquired pneumonia that requires hospitalisation has on the quality of life of patients. The final stage will be to design a generic economic model to evaluate any new
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. Using gastruloids as a model system with which to study GAG structure/function relationships. Generating gastruloids from induced pluripotent stem cells (iPSCs) to create in vitro models for studying
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simulation study of light matter interaction, digital twin enabled process development and life cycle assessment will be researched. Opens: Immediately Deadline: 08/08/2025. Duration: 36 months Funding: Funded
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correction. This machine-learning approach, however, needs a realistic model of light propagation in the retina in order to validate it and to generate the large volumes of training data required. Funding
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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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compatibility with traditional composite matrices. Explore complementary computational fluid dynamics-discrete element method (CFD-DEM) simulations as a tool to predict fibre-fluid interactions and inform
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mitigation strategies to prevent performance losses due to these impurities. We will explore both experimental techniques as well as computational models to provide feedback for designing higher efficient
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, upon receiving applications and rank them in priority order. The University of Manchester will provide CSC and each qualified candidate with a copy of the offer letter. After receiving the offer letter
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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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on cell viability and DDR activation in established human cell models. The student will perform CRISPR screens to determine factors that affect resistance/sensitivity and follow these up with mechanistic