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in ecological modelling, network analysis, dynamical systems, and linking theoretical approaches with empirical ecological data. Ability to work independently while contributing constructively to an
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ultimately shifts in species' distributions. This project harnesses research in ecological and agent-based modelling, machine learning, and AI to increase the predictive power of models of species
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in another way. Strong interest in theoretical ecology, ecological networks, response diversity, ecosystem stability, and mathematical and computational modelling. Experience with scientific
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ecological modelling of biodiverse communities to meet emerging environmental protection goals. First, there is limited understanding of the differences among how terrestrial, aquatic and their interface
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AI and machine learning to ecological questions. Experience with scientific programming, quantitative analysis, or computational modelling, preferably using R, Python, C++, or comparable tools
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ecological questions. Experience with scientific programming, quantitative analysis, or computational modelling, preferably using R, Python, C++, or comparable tools. Interest in working with large ecological
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the integration of both ecological and social data using platforms such as Marxan or prioritizr Experience with agent-based ecological modelling approaches (e.g., using R, NetLogo, or similar) Working
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, natural history collections, long-term biodiversity monitoring, image analysis, climate data and ecological modelling. The doctoral researcher will work closely with Principal Investigator Ellis and will
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Systems) , an agent-based World–Earth model developed with the copan:LPJmL modelling framework (https://copanlpjml.pik-potsdam.de) to investigate the co-evolution of human land management decision
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monitoring, image analysis, climate data and ecological modelling. The doctoral researcher will work closely with Principal Investigator Ellis and will be co-supervised by Prof Tomas Roslin (University