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applications using patient-specific data Very strong expertise in the theory and application of Physics Informed Neural Networks to inverse problems Expertise in sensitivity analysis and uncertainty
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high-fidelity simulation environments and Monte Carlo frameworks to validate estimation and tracking algorithms. Perform statistical analysis of algorithm performance, uncertainty quantification, and
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processing or willingness to learn quickly. Publications, thesis work, or demonstrable projects in computer vision, multi-modal ML, digital twins or biomedical ML. Familiarity with uncertainty quantification
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-based approaches often lack principled uncertainty quantification, limiting their reliability in healthcare applications. This project aims to develop uncertainty-aware LLM methods grounded in
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applied methodologies in Data and Image Analysis, Computational Imaging, Statistical Learning, Uncertainty Quantification, Robust Estimation, and Deep Neural Networks. The group combines expertise in
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quantification, or modelling of biological tissues/porous media. Please email Michal Kalkowski m.kalkowski@soton.ac.uk for any informal enquiries. Where to apply Website https://www.timeshighereducation.com
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PhD project involves interdisciplinary research at the interface of computer science and mathematics and addresses a complex, coupled inverse problem with explicit uncertainty quantification. Research
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. Their performance will depend on the quantity and quality of available data. The aim is a rigorous quantification of uncertainties in the final predictions. Ultimately, the study seeks to determine how PINNs can be
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quantification), and regression analysis to estimate forest variables using forest inventory data and auxiliary remote sensing data; f) Develop maps of forest management variables and their associated uncertainty
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la biodiversité locale, le fonctionnement de l'écosystème et les biens et services qu'il délivre. La caractérisation, voire la quantification, des impacts en terme de bénéfices et de risques pour la