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related to models and multiple sources of data describing ecological dynamics. The PhD project will address the following aims: 1) Develop efficient tools for learning about models from data, 2
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challenges. The ASE integrates earth and environmental life science, ecology, engineering and technology, human ecology, humanities, and the social sciences to address key issues of the environment and
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/Qualifications Previous experience in the analysis and simulation of stochastic processes and stochastic differential equation systems. Previous experience with population dynamics models in ecology. Additional
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statistical and/or causal inference skills; ● Strong background in ecological theory and modeling. ● Experience leading peer-reviewed publications and/or reports; ● Excellent communication and collaboration
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modelling activities of the MOSAIC Project. The ideal candidate will be an organised, independent, and collaborative researcher who can integrate advanced ecological understanding with rigorous data analysis
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statistics; Experience with process-based models in ecology and biogeography; Knowledge of other programming languages (Julia, Python) and cluster-based computing. LanguagesENGLISHLevelExcellent Additional
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that are differentially expressed in genome-sequenced marine model bacteria and natural marine bacterial assemblages. Analyses will involve state-of-the-art techniques in microbial ecology and molecular biology (e.g. gene
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Department of Land Surveying and Geo-Informatics Research Assistant Professor in Geospatial Artificial Intelligence (GeoAI) / Geomatics / Global Change Ecology / Urban Science and Sustainability
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; developing, interpreting, and applying the two statistical models; and submitting peer-reviewed publications and presenting findings at scientific conferences and stakeholder workshops. Unit URL https
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builds on our previous modelling studies (Sentis et al. 2017, Ecology Letters; Dijoux et al. 2024, Ecology Letters) and will utilise our on-site mesocosm facility (Duchet et al. 2024, Water Research