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from end-of-life recycled aluminium to reduce embedded carbon level to below 2 tonnes CO2e/tonne of aluminium as a supplied component and eventually to Net Zero carbon. This project is concerned with
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trajectories between the United Kingdom and India. The candidate will be part of the ERC-funded project ‘Retirement across countries: A tripartite analysis of the welfare state, family care networks, and the
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energy storage within HVDC networks is a promising response to these challenges. Such integration allows HVDC systems to deliver a broad range of new grid services, including fast frequency response
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to the complexity of the mathematical models that describe them. The current consensus is that there are three “types” of viscoelastic chaos: modified Newtonian turbulence, elastic turbulence, and elasto-inertial
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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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can be adjusted upon agreement with the successful candidate). Project Overview The drive for net-zero and sustainable manufacturing is reshaping the future of advanced materials. Traditional composite
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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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the Department of Biomedical Engineering at Swansea University, but you will also interact closely with our national network of clinicians from across the UK. This ensures the project stays grounded in clinical
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glaucoma (POAG) is the leading cause of irreversible blindness worldwide. It arises from fibrotic remodelling of the trabecular meshwork, a filter-like structure in the anterior chamber of the eye. As the
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supporting the Net Zero 2050 target. This PhD project will develop an AI-enabled framework that optimizes wind turbine control and predictive maintenance. Using Deep Reinforcement Learning (DRL), the system