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Field
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synthetic populations and mobility digital twins for resilient and equitable transport planning. The project combines agent-based modelling, large-scale mobility data, transport networks, generative
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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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synthetic populations and mobility digital twins for resilient and equitable transport planning. The project combines agent-based modelling, large-scale mobility data, transport networks, generative
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interest in sustainability transitions and/or urban systems tipping dynamics. Good knowledge of computational modelling and programming. Experience with Python and agent-based modelling is particularly
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to develop learning-based model predictive control (MPC) algorithms for multi-agent multirotor drone navigation around vessels in maritime environments. The role will focus on integrating multirotor
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clinical data, imaging, pathology and outcome data Multi-agent and predictive AI development: Develop machine-learning components for patient-trajectory modelling, recurrence and survival prediction
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advances in the modelling of deception, using information-theoretic techniques, allow deceptions to be easily incorporated into evolutionary simulations. Agent-based modelling and information-theoretic
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suitable for agent-based modelling. The PhD student will ensure that formalisations remain faithful to the underlying psychological theory—a translational role that is central to the project's
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with researchers across the inter-discplinary team Experience / interest in Agent-based modelling or synthetic user research platforms Social network principles like preferential attachment, opinion
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for extension. We are looking for someone to join our team of consumer behaviour scientists and agent-based modellers who can bring knowledge of and experience with modelling cognitive or behavioural