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developing/adapting computational models or algorithms to analyse biological data, and experience with single cell or spatial omics datasets or knowledge of cancer biology would be an advantage. What we offer
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of the project will seek to develop deep learning models (e.g., adapting methods in [6]) based on spatial cellular graphs constructed from these images to predict clinical outcomes. The research will be carried
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the ecology of infectious diseases, and thereby benefiting greatly from overlap with strengths in spatial and quantitative ecology. Our modelling is developed in close proximity to data, and focused
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defining environmental policies such as setting biodiversity targets. This project will aim to construct spatial models of biodiversity, explicitly accounting for the temporal structure of the data
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the project to have well-distributed data both in space and time. This will ultimately lead to higher quality (more spatially and temporally accurate, complete, precise) 3D models. However due to the complexity
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well as in liver regeneration and cancer. Leveraging publicly available spatial datasets, this project aims to develop data-informed computational modelling and systems biology framework to dissect molecular
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the Drosophila larval neuromuscular junction, a well establish model system for uncovering the basic molecular mechanisms that go wrong in human neurodegenerative diseases, on account of the high degree