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-driven model selection, and deep learning for data analysis and feature extraction from characterisation data. Surrogate modelling will be employed to reduce computational costs, and AI-based uncertainty
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in porous geological formations. The successful candidate will develop and implement computational models, validate them against experimental or field data where available, and contribute to the design
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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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that you apply early as the advert may be removed before the deadline. This PhD project aims to develop a virtual tabletting laboratory by creating computational models that capture the multiscale mechanics
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: machine/deep learning, numerical modelling, statistics, optimisation, scientific computing • Ability to work across disciplines and collaborate with academic and industrial teams Desirable: • Experience in
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workflows and contribute to building UK capability in an important advanced reactor area. The ideal candidate will enjoy computational modelling and quantitative problem‑solving, with a strong foundation in
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platform and/or its manipulator(s) will be used to trace the emission source, using a combination of sensor data, gas behaviour models, and robotic navigation techniques. The project can be tailored
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extreme threats to national security. AWE has pioneered advancements in areas including physics, engineering, materials science, and high-performance computing. Together we’ve helped shape the UK’s
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infrastructure. The successful candidate will benefit from access to extensive expertise across The University of Manchester in civil engineering, structural engineering, fire engineering, computational modelling
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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