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Field
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Embark on a ground-breaking PhD project harnessing the power of Myopic Mean Field Games (MFG) and Multi-Agent Reinforced Learning (MARL) to delve into the dynamic world of evolving cyber-physical
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alternative models where existing methods prove inadequate. This project is suitable for Engineering or Physics graduates with a strong background in fluid mechanics and heat transfer, preferably with
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Supervisory Team: Leonardo Aniello, Han Wu PhD Supervisor: Leonardo Aniello Project description: Blockchain and Federated Learning (FL) are two emerging technologies that, when combined, offer a
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relevant discipline such as materials science, aerospace engineering, mechanical engineering, chemical engineering, physics, or related fields. Prior experience in materials research is beneficial but not
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requirements Applicants should have a first or second-class UK honours degree or equivalent in a relevant discipline such as aerospace engineering, mechanical engineering, physics, or related fields. Prior
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relevant discipline such as materials science, aerospace engineering, mechanical engineering, chemical engineering, physics, or related fields. Prior experience in materials research is beneficial but not
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equivalent in a relevant discipline such as aerospace engineering, mechanical engineering, electrical engineering, computer science/engineering, applied physics/mathematics, or related fields. Prior experience
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distribution. This process often takes place in large scale driers where the material is heated and broken up mechanically with mixing blades. However, under certain conditions the process can break down as the
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Postgraduate Programme of Study (soton.ac.uk) Select programme type (Research), 2025/26, Faculty of Engineering and Physical Sciences, next page select “PhD Computer Science (Full time)”. In Section 2
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PhD studentship: Understanding the retention and treatment of highway runoff pollutants within roadside bioretention systems – and implications for design and long-term maintenance Award Summary