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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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are seeking a Ph.D. student to join our multidisciplinary team developing a radical solution for better detection and treatment that uses ultra-thin snake-like robots and advanced optical imaging techniques
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Novel optics and AI approaches to image the centre of a live root for the first time. This exciting opportunity is based within the thriving Optics and Photonics Research Group in Faculty
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to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show
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and execute tasks with multi-modal heterogeneous data (e.g. text, location, and images), associated with diverse applications, such as earth observation, climate, and phenotyping. Developed models will
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oxygen status under contrasting environmental conditions. • Application of imaging- and sensor-based approaches to visualise and quantify oxygen dynamics in roots and their surrounding environment
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oxygen status under contrasting environmental conditions. Application of imaging- and sensor-based approaches to visualise and quantify oxygen dynamics in roots and their surrounding environment. Analysis
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will have access to world‑class facilities, including advanced imaging at the Nanoscale and Microscale Research Centre, bespoke tack‑testing equipment, and state‑of‑the‑art composite manufacturing
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the form of coatings, ablation and high-heat-flux testing rigs, and characterisation using secondary electron imaging, X-ray diffractometry, electron backscattered diffraction, transmission electron