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of knowledge-driven models, leveraging Bayesian statistics and causal inference for calibrated uncertainty, distribution-shift detection, and safety guarantees. You will be will working within the Center
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data with field-collected biodiversity and ecosystem functioning measurements to understand how biodiversity and ecosystem functioning are linked and changing across different habitats in response
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version control and containerization (Docker/Singularity) Statistical Modeling: Quantitative data analysis using GLMs, Bayesian methods, or mixed-effect models to interpret complex perturbation datasets
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to understand how biodiversity and ecosystem functioning are linked and changing across different habitats in response to climate change and/or human use of natural resources. The successful candidate will
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of knowledge-driven models, leveraging Bayesian statistics and causal inference for calibrated uncertainty, distribution-shift detection, and safety guarantees. You will be will working within the Center
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of the Middle to Upper Palaeolithic transition and the complexities in the expansion and extinction of different forms of human, Training in radiocarbon dating and its application to archaeology, pretreatment
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into aspects of the Middle to Upper Palaeolithic transition and the complexities in the expansion and extinction of different forms of human, Training in radiocarbon dating and its application to archaeology
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will be grounded in rigorous mathematics coupled with a sound understanding of the underlying earthworm ecology. Bayesian inference methodologies will be developed to estimate where and when behavioural
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individual rates of ageing. Role You will extend BrainAGE from global estimates to regional normative models using Bayesian regression and GAMLSS to derive age- and region-specific reference distributions
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in practice. You will also work alongside a diverse team of researchers and stakeholders from different disciplines and sectors, gaining valuable experience in interdisciplinary collaboration and