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(Dr Jun Jiang) (2) In-situ formability, microstructure analysis and forming process optimization (Prof Li-Liang Wang) (3) Crystal plasticity modelling to understand how microstructural features caused
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based at the Department of Obstetrics and Gynaecology, University of Cambridge. They investigate the mechanisms by which sub-optimal nutrition in early life can affect reproductive ageing, the impact of
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workshops as a means to continuously improve technical and theoretical knowledge. Ability to obtain information from literature and from colleagues and integrate this into developing and optimizing work
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—such as solar arrays, antennae, and habitat frameworks—while minimizing launch mass and deployment complexity. Key objectives include optimizing structural design for deployment efficiency, resilience under
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reactors, can be optimized for N2O mitigation or ultimately complete N2O removal. Overall, the project represents a unique opportunity to engage with the water utility sector with regards to greenhouse gas
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optimise a ‘Digital Twin’ of the Tees estuary to ensure that the NBS are deployed at locations optimal for performance and longevity while operating within the constraints placed upon deployment by other
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process. Address blind inverse problems by defining a network to learn distortion functions from data, informing the optimization in the learning process. Refine optimization and learning strategies
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more adaptable to other chiral systems. Understanding how these two systems can be optimized and integrated, including effective solvent exchange and recovery, is crucial. Efficient solvent management is
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aims to address these inefficiencies by developing innovative technologies for cleaning optimization and water reuse. The research will explore the use of ultraviolet (UV) fluorescence imaging to monitor
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sensors to understand their local surroundings at sea and inform optimal action. To ensure safety requires the ability to reliably detect, image and recognise their environment, in terms of surrounding sea