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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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dynamics and tissue morphogenesis during embryo development using cellular, molecular and mechanical approaches. Cell movements underlie tissue patterns and shapes. Using chick embryos as the model system
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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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yeasts collected from around the globe and offers the opportunity to better characterise these novel isolates whilst seeking a practical solution to an economically important spoilage problem. The post
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modelling of laser shock peening. Molecular Dynamics (MD) and Finite Element (FE) simulations will be combined to account for the complex physical phenomena and their different scales. The interdependence
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Establishment). Recent work by the group (leading to REF 4* rated outputs and several Keynotes) has contributed to bridging the gap between Computational Solid and Fluid Dynamics, with a unified computational
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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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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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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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proud of. A short statement on your research interests for the future. Tell us what you dream about, scientifically (motivation letter). Two letters of recommendation (directly send to: caslav.brukner