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Predicting infiltrative glioblastoma progression using advanced magnetic resonance imaging methods Project Supervisors: Michael Chappell, Steffi Thust Project Overview Glioblastoma (GBM) is the most
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at the tumour margin represent a key target for earlier and more effective therapeutic intervention. This PhD project will develop advanced MRI analysis methods and imaging-driven predictive models focusing
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Towards a Flower Waste Biorefinery: Predictive Enzyme Design for Bio-Based Chemicals This exciting opportunity is based within the Faculty of Engineering, which conducts cutting-edge research in
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-order predictive models. The student will gain hands-on experience in industrial applications, including practical aspects of aeroengine oil system design, spending part of their PhD based on-site
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techniques for building lower-order predictive models. The student will gain hands-on experience in industrial applications, including practical aspects of aeroengine oil system design, spending part of
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and conduct laboratory experiments to assess graphite smoulderability, develop physics-based models to predict scalability, and perform techno‑economic analyses and life‑cycle assessments using machine
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characterisation and AI‑assisted modelling. Working within the Composites Research Group, you will develop a digital twin of the PFA cure process, combining mechanistic modelling with neural‑network‑based prediction
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complex physical systems to perform tasks such as prediction, classification, and signal processing. However, one major challenge remains: We still do not fully understand what makes a reservoir computing
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develop a digital twin of the PFA cure process, combining mechanistic modelling with neural‑network‑based prediction of complex behaviours such as void formation and brittleness. In parallel, you will
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of energy and computational power. Reservoir computing offers a promising alternative by using complex physical systems to perform tasks such as prediction, classification, and signal processing. However, one