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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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, which are essential for safe operation in these challenging aerospace environments. You will develop robust, physics-based models to analyse failure, with a focus on understanding mechanical and
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University colleges, and discounts at University museums. See https://hr.admin.ox.ac.uk/staff-benefits Application Process Applications for this vacancy are to be made online. You will be required to upload a
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or international equivalent in an appropriate subject, including: biomedical sciences, medical physics. Further qualification such as an MRes is advantageous. If your first language is not English, you need
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Candidate Guidance to provide support on the application and interview process. Discover our benefits, visit Your Benefits website. We welcome applications from UK, Europe and worldwide and aim to make your
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of Birmingham. The School is a vibrant community of researchers whose interests span a range of chemical disciplines. Application process: Interested candidates can contact Dr. Maguire (o.r.maguire@bham.ac.uk
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graduate entry requirements which include holding at least an upper second-class degree or equivalent qualifications in a relevant subject area such as mechanical engineering, physics, materials science, or
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functional performance of the components and the key process parameters. The project will deal with the design of special process setups, testing its working principles and performances followed by
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process. Please quote reference NM46253 on your application and in any correspondence about this vacancy. The University actively supports equality, diversity and inclusion and encourages applications from
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learning (ML) techniques to gain new physical insights into fundamental questions about viscoelastic flows in both canonical configurations and porous media applications. ML techniques will be leveraged