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are they talking about, what do they want, need, and value? What are they concerned about? What AI techniques and intelligent agents can we develop to work supportively and/or unobtrusively along with them
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scenarios. Your research will map multilevel governance structures and you will co‑create mitigation strategies through participatory workshops. You will model farmers’ adaptation behaviour using agent‑based
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modelling (e.g., agent-based Bayesian models, cognitive learning models, machine learning). Experience in annotation software such as ELAN and PRAAT. Existing peer-reviewed journal publications and conference
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and geometric deep learning, or simulation-based inference. We welcome your unique perspective and are eager to learn how your track record, educational vision, and future research goals align with
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from this PhD project into an agent-based model. This model will be developed by other PhDs in the project team and simulates household adaptation behaviour over time in global flood-prone regions
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. You will then develop a procedure to translate these quantified relationships to a predictive agent based model for the investigation of animal movement behaviour under future climate scenarios. We also
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operation including maintenance. Our research builds on a variety of methods, ranging from analytical methods to fast-time traffic, or agent-based, simulations, and from the newest operations research methods
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governance, simulation and modelling (agent-based modelling, discrete-event simulation, network models), data modelling / interoperability (e.g., ontologies/semantics, data standards, API-based exchange
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/ Robust) Combinatorial Optimization, Game Theory, and Network Theory, as well as Artificial Intelligence. Potentially, scenarios could be simulated using agent-based, discrete-event, or other techniques
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outcomes under different market design scenarios. The research will combine machine learning, stochastic optimization, and agent-based modelling with behavioural experiments. Case studies from emerging