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modelling of climate-sensitive infectious diseases, with a particular emphasis on Bayesian hierarchical modeling using Integrated Nested Laplace Approximation (INLA). The work will contribute to ongoing
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available on the development of spatiotemporal statistical modelling of climate-sensitive infectious diseases, with a particular emphasis on Bayesian hierarchical modeling using Integrated Nested Laplace
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, non-parametric methods, machine learning, hierarchical Bayesian modelling, and time- and space-modelling. The group emphasizes general methodological development, often motivated by real-world
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appropriate conditions, it provides a confidence set (credibility set if prediction is Bayesian) for a multivariate estimate with statistical coverage guarantees. This PhD project aims to develop new CP methods
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); mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; and statistical machine learning. More about the position The position is
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. FATES simulates and predicts growth, death, and regeneration of plants and subsequent tree size distributions by tracking natural and anthropogenic disturbance and recovery. It does this by allowing
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track record experience in collection-based research (both physical and/or digital) teamwork and networking skills Personal skills: We are looking for a highly motivated, creative, and structured
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MIMICS+ module for soil carbon decomposition. FATES simulates and predicts growth, death, and regeneration of plants and subsequent tree size distributions by tracking natural and anthropogenic disturbance
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that the candidate has (and can document): a strong academic track record experience in collection-based research (both physical and/or digital) teamwork and networking skills Personal skills: We are looking for a
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defense are eligible for appointment. Fluent oral and written communication skills in English. A strong track record in fundamental research in modelling of biophysical phenomena, capillary flows or wetting