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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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and impact related to two EPSRC funded projects: ResTOrES (Resilience Toolkit for Offshore Energy Systems) and RENEW (Climate Resilient Heat Electrification for Net-Zero Emission Whole Energy). The
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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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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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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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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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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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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
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advanced simulation methods, including Reynolds-Averaged Navier-Stokes (RANS), Direct Numerical Simulations (DNS), and/or Large Eddy Simulations (LES), will be employed to accurately model the complex flow
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-processing crucial. However, video restoration and enhancement are complex due to information loss and the lack of ground truth data. This project addresses these issues innovatively. We propose using prior