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Field
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environmentally viable at scale. Research Aims: Process Intensification & Scale-Up Design and evaluate scalable MEC architectures (cassette and containerised). Use CFD modelling and lab pilots to optimise
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million individuals has never before been possible in genetics research, not even in model organisms, and promises to transform the way we think about evolution. The student will receive broad training in
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model), Imaging data acquisition and analysis (Positron Emission Tomography) Complementary training includes research ethics, scientific communication, project management, and collaboration across
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production-grade system that integrates Vision Transformers for visual deepfakes, advanced Natural Language Processing (NLP) models for phishing detection, and a dedicated Explainable AI (XAI) layer
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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containerised). Use CFD modelling and lab pilots to optimise hydraulics, mass transfer, and electrode configurations. Energy Integration Quantify full energy balances of MEC operation. Explore integration with
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tools and models of integrated health and social care. The successful Research Assistant will support the team and co-develop a PhD project within the remit of the workstream. Research priorities and
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approaches to interpreting these large datasets, as well as computational models that capture low-dimensional structure that reflects the architecture of the neocortex. By working with researchers developing
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combining high-fidelity computational modelling with artificial intelligence to overcome key barriers in performance. The investigation will focus on optimising core gas exchange and combustion processes
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create a working framework that includes both experimental and modelling prototypes, including AI/ML tools to assist with the large number of variables involved. This project is seeking candidates with a