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systems to act as an oversight of the AI. This is costly, complex, and time consuming, nullifying the benefits of using an AI approach. This project’s two aims are (1) Establish the best approach
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to analyse complex datasets, extract meaningful insights, and guide the optimisation of drug molecules. Collaborate with internal groups, including the Centre for Additive Manufacturing (CfAM) to design and
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Join our diverse and inclusive team to transform the future of aviation as part of the UK’s EPSRC Centre for Doctoral Training in Net Zero Aviation. Offering fully funded, multidisciplinary PhD
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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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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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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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categories for a better capability of managing the uncertainty related to system complexity and data availability to achieve more accurate RUL estimations The student will have the opportunity to work with
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with a wide network of stakeholders, and explore new avenues for medical applications. For ongoing work and publications on this project, please see our website: www.cnnp-lab.com . This is a 12-months
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modelling capabilities for the prediction of energy extraction efficiency, especially focusing on improving the understanding and prediction of the complex flow phenomena, including buoyancy effects in AGS
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the scalability and robustness of AI in complex environments which is a major step towards the digital transformation of the manufacturing industry. Motivation Automation is key to meeting the growing demand