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20th October 2026 Languages English English English The Department of Biology has a vacancy for a PhD Candidate in Modelling Marine Larval Dispersal and Spatial Population Genetics and Demography
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on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured
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to characterize forests and their biodiversity. The research will focus on developing multimodal learning approaches that combine complementary forest information across data sources, spatial scales, and time. A
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on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured
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and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured biological data are increasingly common in modern
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, yet spatial planning stakeholders insufficiently consider these insights. Few studies systematically combine methods and insights from these diverse approaches, and an evidence-based spatial and visual
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and needs-based design, yet spatial planning stakeholders insufficiently consider these insights. Few studies systematically combine methods and insights from these diverse approaches, and an evidence
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of the position Complete doctoral education leading to a PhD degree. Identify case studies where risk and vulnerability intersect and collect relevant spatial and non-spatial datasets. Co-create vulnerability
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. Design explanation methods that connect predictions to meaningful spatial, temporal, frequency-domain, semantic, or example-based evidence. Evaluate robustness and transfer to unseen datasets, content
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design methods. Understanding of geospatial variability in geological or rock mass properties, including spatial correlation, anisotropy, and heterogeneity. Familiarity with machine learning/AI techniques