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, including time series analysis and statistics (e.g. mixed effects modelling) Capacity to develop computer code and experience with programming languages (Matlab, Python, R) and geospatial tools (e.g. ArcGIS
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representations Analysis of structure–function relationships between morphology and movement Modelling genome–phenotype relationships using machine learning and genomic language models The project offers a unique
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training in: genome biology and evolutionary genomics population genetics and conservation genomics computational analysis of large datasets The work will primarily be computational in nature, requiring
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population genetics and conservation genomics computational analysis of large datasets The work will primarily be computational in nature, requiring development of a broad technical skillset. This project is
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. Research will have a strong focus on computational analysis or predictive modelling of pathogen biology or host-microbe systems for which multidimensional, genome-scale experimental data are now available or
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perturbation-based GRN inference for single-cell and spatial multi-omics data, to boost GRN quality and add the cell type and tissue heterogeneity dimensions to causal regulatory analysis. A deep learning
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: machine learning, data analysis, statistical modelling, explainable AI, computational methods for large-scale data, and analysis of biomedical or population-based datasets. An interest in applications in
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social scientific data with ecological analysis from the “Blue Leads Green ” project. About the position The PhD student will have the freedom to co-design their project that may explore questions related
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diagnostics. The work includes analysis of genomic, transcriptomic, and DNA methylation data across different tumor subgroups, using both in-house and public cohorts, with the aim of identifying novel molecular
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data integration, analysis, visualization, and data interpretation for patient stratification, discovery of biomarkers for disease risks, diagnosis, drug response and monitoring of health. The precision