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co-supervised by both academic and industry experts, gaining valuable skills at the interface of hydrogeology, environmental engineering, and computational modeling. Research Objectives (1
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begore the deadline. The start date is 1st October 2025. This studentship is related to a multi-institutional EPSRC Programme Grant "AMFaces: Advanced Additive Manufacturing of User-Focused Facial
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Dr Sendy Phang. The student can gain experience and skills in a range of topics, such as Artificial Intelligence and Deep Learning, nanofabrication, computational modelling, metamaterial design, and
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Dr Sendy Phang. The student can gain experience and skills in a range of topics, such as Artificial Intelligence and Deep Learning, nanofabrication, computational modelling, metamaterial design, and
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challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and
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mitigation strategies to prevent performance losses due to these impurities. We will explore both experimental techniques as well as computational models to provide feedback for designing higher efficient
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a comprehensive, multi-fidelity suite of liquid hydrogen (LH2) pump models to predict and analyze pump performance, stability, and its interaction with the broader fuel system architecture for a
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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
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sustainability goals whilst improving operational efficiency? This PhD studentship will involve developing machine learning models, creating virtual manufacturing replicas, and implementing optimisation algorithms
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computation, with potential links to hydrogen engine research and broader digital twin technologies. You will gain expertise in: Computational modelling of materials (e.g., FEM, crystal plasticity, or phase