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to the development of multiscale computational models for simulating crack propagation and establishing reliable methods to predict the residual strength of composite structures. The simulations, performed in Ansys
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to short-term spatiotemporal extreme wind speed prediction, addressing limitations in conventional models. The candidate will: Analyse Extreme Data Distributions: Apply advanced statistical techniques
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of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
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theory, and the analysis of large data ensembles. You will write papers for submission to academic journals, collaborate with academics and PhD students, and communicate your research at national
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optimization techniques, coding new algorithms, creating new mathematical theory, and the analysis of large data ensembles. You will write papers for submission to academic journals, collaborate with academics
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generative model-based domain translation, in collaboration with leading research institutions. This new studentship aims to develop the next generation of interpretable and cross-modal predictive models
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outcomes. By mapping these gene distributions and integrating them into a predictive tool, the project seeks to stratify patients as likely responders or non-responders to chemotherapy, enabling personalised
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of photons from an ensemble of atoms, and time-crystalline ones, marked by emergent self-organised oscillations. Beyond their fundamental academic interest, these phases are also receiving particular
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predictive accuracy and prohibitively long computational times, making them unsuitable for real-time process control. Artificial intelligence (AI) models present a promising alternative by addressing
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perpetuation (or maintenance/persistence); to build ML models that include the heart’s physical properties to find patterns in the data and predict which areas in the heart drive AF. This project will explore