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Overview Accurate modelling of the solar atmosphere is essential for understanding the energy flow and accumulation that underpin the heating of the solar chromosphere and corona, and for uncovering
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driving through deep reinforcement learning. Computing demands can grow rapidly with such models, so a significant aspect of the research is in formulating the problem in a tractable form, and application
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different supernovae models. In addition to these analysis topics, students have the opportunity to engage with the scientific programme of work that the Sheffield group is pursuing for both the Super-K and
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organised specifically for the appointed person, in terms of the split between clinical and academic work. The post represents an exciting opportunity to combine clinical work with a programme of high-quality
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lead and perform research in these areas, who will have practical experience of blast experimentation, underwater blast loading, and demonstrable computational modelling experience in a relevant area
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PhD at the Forefront of Computational Solid Mechanics and Machine Learning School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Dr J L Curiel Sosa Application
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”. This exciting opportunity involves leading the development of advanced data-driven mathematical and computational models to suppress turbulence in pipe flows, contributing to pressing engineering efforts toward
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performance. Research in the group is a mixture of experimental and computation work. A current key focus of the group is development of new multiscale modelling approaches, coupled with data driven modelling
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Deadline: 20 June 2025 Details R2T2 is a UKSA-funded doctoral training programme dedicated to academic research in rocket propulsion, with an emphasis on the acquisition of the practical skills required
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of agricultural weeds to herbicdes from an eco-evolutionary perspective. This project will develop models for the evolution of herbicide resistance that combines field data and computer models. The aim is to