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simulations, exploring novel aspects of numerical modelling and expanding the computational mechanics capabilities of the group. This project offers the opportunity to join a vibrant research group and
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capture technologies. In this project, you will: Develop a 3D Digital Model: Create an advanced computational model of high-pressure mechanical seals. Apply Computational Fluid Dynamics (CFD): Simulate gas
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focus on weather-related extremes, much of it by our team, however we must also consider their cyber risks. Network modelling will be used to simulate a range of different cyber and natural hazards
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. The research will combine computational modelling, experimental validation, and machine learning techniques to develop a predictive phenomenological PAC model. The successful applicant will develop and apply
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exciting opportunities for knowledge exchange and networking with world leading ocean scientists. For example, you will help linking the observational data to new model simulations being developed
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Start date: 1st October 2025 GenAI-Accelerated Kinetic Modelling of Catalytic CO2 Valorisation Applications are invited for an EPSRC-funded project available from October 2025, to work under the
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alongside numerical simulations relying on high-performance computing and reduced order modelling. We aim to gain new insights about the physical coherent structures which are most relevant to viscoelastic
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manufacture, to enable quantitative imaging. Your research will include a mix of computational and experimental work to develop and characterise these instruments. Monte Carlo simulations (using GEANT4) will
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the pathogenic mechanisms of amyotrophic lateral sclerosis (ALS)/frontotemporal dementia (FTD) using in vitro & in vivo (mouse and fly) models. We develop stem cell-based models for these neurodegenerative
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PhD Studentship: Optimisation of Liquid Metal Filtration and Cleanliness in Nickel Based Superalloys
-supervision by Dr Mark Hardy. The industry aligned EPSRC DigitalMetal CDT offers a four year training programme on integrating data driven with physics-based models of products equipping students with