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become part of a dynamic academic community, work alongside global experts, and make a meaningful impact on research and society. THE OPPORTUNITY College of Engineering & Computer Science is looking
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. Project details In this project we aim to develop graph deep learning methods that model spatial-temporal brain dynamics for accurate and interpretable detection of neurodegenerative diseases
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-sensory perceptions, design features and vehicle dynamics. A focus on these factors not only has the potential to improve customer satisfaction but also reduce driver workload, increase usability and
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to symmetry breaks in the patterning process. A hybrid modelling approach integrating the dynamics of a core network while utilising a virtual template from experiments for cellular growth and division will be
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season properties (e.g. number, intensity) for lead times ranging from one to approximately six months in the latest generation of dynamical seasonal and decadal forecast models. Seasonal forecasts
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role: This post, funded by an ERC Consolidator Grant (DISCO), focuses on the dynamic behaviour of ferroelectric domain walls using advanced cryogenic and electrically biased 4D scanning transmission
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to symmetry breaks in the patterning process. A hybrid modelling approach integrating the dynamics of a core network while utilising a virtual template from experiments for cellular growth and division will be
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candidate will conduct fundamental research aimed at understanding cell shape dynamics and the cross -talk between cell shape and cell state during epithelial-to-mesenchymal transition (EMT). The aim
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factors influence this, including multi-sensory perceptions, design features and vehicle dynamics. A focus on these factors not only has the potential to improve customer satisfaction but also reduce driver
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Modelling post combustion amine CO2 capture plant School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof Mohamed Pourkashanian, Prof Lin Ma, Dr Kevin Hughes