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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
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will undertake several research activities. These include the development and implementation of algorithms for self-supervised denoising and artifact removal in EM images, which may involve modeling
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machine structures, together with AI-driven optimization frameworks for diverse applications while considering LCA metrics. The success of this project could serve as a model for other energy-related
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develop data-inspired and data-driven models of sarcomere assembly. This will involve mean-field models and agent-based simulations. Additionally, depending on your aptitude, you can analyze topological
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biological physicists who ask how physical mechanisms shape functional biological patterns. We combine statistical physics, nonlinear dynamics, mathematical modeling and data-driven simulation with physics
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backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services
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scenarios such as shock loading, poses a significant challenge in engineering, as existing models often fail to represent the complex interplay of plastic deformation, strain localisation, and void formation
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strong theoretical and numerical foundation in FEM, with applications in adaptive and performance-driven design. The work supports the broader goal of transforming how engineers and architects
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is to address these challenges by developing innovative integrated chassis controllers and processes that seamlessly coordinate multiple actuators from the outset. The research will explore model-based
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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case