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Experience in some/all research methodologies relevant to the post such as analysis of soil organic compounds, microbial community analyses, techniques to manipulate mycorrhizal fungal interactions and
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appreciated. • Experience in single-cell or single-nucleus sequencing would be appreciated. • Experience in spatial transcriptomics would be appreciated. • Knowledge of gene network analysis would be
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, bioinformatics, and data analysis. - Participation in an innovative project focused on epigenetics and the spatial organisation of the genome. - The opportunity to work with state-of-the-art genome-mapping
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or expand include: metabolism (spatial-biology, metabolomics, metabolic flux analysis, and artificial intelligence), structural biochemistry (macromolecular structure using cryo-EM, X-ray crystallography, and
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through the analysis of oceanographic samples, interpretation and synthesis of multidisciplinary datasets, dissemination of research findings through publications and presentations, and archiving of data in
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disease. Apply and optimize advanced spatial biology techniques, such as spatial transcriptomics, proteomics, and multiplexed imaging, to analyze brain tissue. Lead data analysis and interpretation
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studies, critical infrastructure studies, heritage research, and spatial analysis. The PhD position is funded for three years, starting 1 March 2027 and ending 28 February 2030, and is embedded within
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Computational Sciences or similar. You have strong expertise on analyses of biology-related large datasets. Expertise in single-cell and spatial data analysis, spatial statistics and annotation is an advantage
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and how disruption of these processes contributes to congenital heart defects. The project will combine state-of-the-art single-nucleus RNA sequencing, spatial gene expression analysis and molecular
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, electrical polarisation and, then evaluate cell-matrix interactions using live-cell imaging, molecular assays and, spatial transcriptomic approaches to identify key mechanisms linking matrix mechanics