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Field
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fluid mechanics, computational geometry, meshing, computational graphics, computational vision, or scientific machine learning in general. Successful candidates will join a community of researchers in
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with GenAI industry and open-source communities. Key Responsibilities: Conduct the research in AI/ML domains, especially in probabilistic ML and scalable sequence network architectures Produce academic
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Multimodal Models Generative AI, Agentic AI, Physical AI, and Embodied AI Trustworthy AI, including explainability, auditability, and privacy Edge AI and model optimisation Physics-informed neural networks
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bringing together the University of Plymouth, NHS partners across Plymouth and Somerset, and a wide network of collaborators, including King’s College London. The programme aims to transform how routinely
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Embodied AI Trustworthy AI, including explainability, auditability, and privacy Edge AI and model optimisation Physics-informed neural networks (PINNs) and surrogate modelling Time-series modelling and
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fractures can play a key role on production performance. One of the persisting challenges is to create complex 3D fracture networks and solve multiphase flow efficiently. None of the existing tools can
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on Artificial Neural Networks and Gaussian Process modelling, to accelerate processing optimisation. Consolidate experimental, techno‑economic, and sustainability data into robust technical evidence packages
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opportunities. Support the attraction and development of high-value, industry-relevant projects for SIT, Polytechnics, and ITE applied research and innovation centres. Leverage industry networks to facilitate
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-cloud computation offloading algorithms. Set up network testbeds integrating hardware, software, and communication protocols to validate edge-only, cloud-only, and edge-cloud computation offloading
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materials. 4. An established network for collaboration in materials characterization, device fabrication, and application development. 5. Independence, initiative, and strong time-management, planning