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learning, with experience in time series forecasting or related applications. You have experience with or a strong interest in graph neural networks, Bayesian methods or uncertainty quantification techniques
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topological optimization framework to achieve optimal structural and functional performance. In parallel, uncertainties quantification and qualification of key design parameters will be carried out to assess
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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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in mechatronic hardware and software You have a solid foundation in probability and statistics for Bayesian modelling, uncertainty quantification, and causal inference You have a team player mindset, a
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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