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research with numerical modelling. The group also has a strong track record in data-driven modelling of unsteady flows. This project explores how paired floating vertical-axis wind turbines can enable wind
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for or astronomical datasets from radio telescopes. Demonstrated radio astronomy research experience in HAA’s target research areas and/or areas directly relevant to the capabilities and science of next-generation
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-essential components of the problem, thereby reducing the size of the subproblems and accelerating the overall solution process. The second line targets the convergence issues often encountered in column
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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
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enhance system reliability and safety, aligning with the UK’s NetZero targets. Aim You will have the opportunity to build a high-fidelity process simulation and perform experimental validation to assess
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for investigating failure mechanisms in CFRP sleeves. Your contribution will help prevent catastrophic magnet detachment and significantly enhance system reliability and safety, aligning with the UK’s NetZero targets
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of the MICRO-PATH doctoral training unit is to tackle these challenges in a focused way and to lay the foundation for establishing the microbiome as a therapeutic and preventative target in the future
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mitigating these statutory micropollutants. Following the monitoring, it is also essential to develop enhanced NbS strategies that target micropollutant removal and remain compatible with other ecological and
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sources, information (mis)alignment, domain shift, missing data modality, data privacy, data and computing cost. It will focus on targeted scientific problems to test the solutions, aiming at a lowest
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lines; generation of samples for proteomics analysis Data integration and analysis, including transcriptomic, proteomic and library screen data Target validation by CRISPR-Cas9 knockouts in primary AMLs