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PhD Studentship available on the RAINZ CDT programme at The University of Manchester. Project Overview Abstract: Offshore wind and marine energy assets operate in harsh, inaccessible environments
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PhD Studentship available on the RAINZ CDT programme at The University of Manchester. Project Overview Abstract: Complex software-driven systems, including autonomous robotics, are typically
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simulation results with experimental data. This project will integrate advanced AI techniques, including machine learning for parameter optimisation (e.g., Bayesian optimisation, reinforcement learning), AI
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in the groups of Dr Florence Hardy and Prof Anthony Green, University of Manchester, as part of the cross-institutional BioAID Doctoral Training Programme, including world-leading experts from Queen's
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modelling and oomph-lib for continuum mechanics simulations, enabling the integration of discrete and finite element methods. Coupled with machine learning techniques, this approach will address the complex
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industries like pharmaceuticals, food processing, and construction, the project may also incorporate machine learning methods for model calibration and optimisation, driving more sustainable material handling
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-driven AI models that capture the underlying process–structure–property relationships governing metal additive manufacturing. By combining mechanistic modelling, in-situ sensing, and machine learning
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, stiffness loss, damage evolution, and transient creep interact under coupled loading. The project will develop temperature-dependent constitutive models informed by numerical simulation. Machine learning
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this challenge head on by combining quantum-mechanical calculations with state-of-the-art machine learning (ML) methodologies to explore and optimise the compositional space of complex high-entropy metal oxides
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performance. This PhD project aims to develop a data-driven framework for graphene aerogel design by integrating structured experimental Design of Experiments (DoE) with machine learning (ML). The student will