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Field
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recovery in critical applications, including aerospace, healthcare, and industrial automation. Research Focus Areas: Predictive Analytics for Fault Detection: Develop AI models that predict potential system
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changes (so called swelling). Swollen batteries are at risk of rupturing which may significantly shorten their lifetime. Development of advanced computer models is critical for understanding and
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in healthcare service and opportunities for identification of such deviations using computer vision approaches. It will demonstrate how deviation data can be used in computer-based simulation models
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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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hardware and data handling toolchains. The research will also involve modelling, analysis and characterisation of the system and components. The PhD will include a placement of minimum 3 months with Team
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for An enthusiastic, self-motivated individual with an interest in empirical and modelling work to test out new reactor designs. This will involve some work with Matlab or similar program to quantify mixing systems
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About us Primary Supervisor: Dr Rebecca Baggaley Applications are invited for 1 PhD Studentship for students with a Home Fees status, based at the Institute of Health Informatics, UCL
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either of these species is likely to affect its onward behaviour, and data on these processes will support predictive modelling. The PhD student will be a part of the Surrey/AWE Centre of Excellence in
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into pilot and large scale pilot systems with partners in the consortium. Aim This project will focus on modelling and testing of new reactors with a view to optimising the best systems for mixing
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Deployment The PhD programme offers: Training in the theory for solar energy technologies, experimental measurement and evaluation techniques, tools for modelling and predicting PV generation. Opportunities