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to simulate sewer networks as dynamic systems, targeting ≥90% modelling accuracy. Train an explainable decision-making agent to optimize interventions (e.g., pipe upgrades), balancing cost, equity, and
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direction could be to use the technique of Inverse Reinforcement Learning (IRL) [2], [3]. IRL is an AI-based technique that supports imitation of the preferred system behaviour by using its behavioural
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. Validation of new types of markets (both those designed above and others) through principled multi-agent simulations, complex systems analysis or other data-driven simulation methods. Fundamental AI techniques
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to the following: Develop a comprehensive understanding of the oxidation kinetics and mechanisms using different oxidising agents. • Develop a comprehensive understanding of the leaching kinetics and
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that would give you an advantage) Experience in computational modelling (e.g., agent-based Bayesian models, cognitive learning models, machine learning, robotics). Experience in annotation software such as
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interact to shape an entire simulated organ. To calibrate and parametrize the models, you will have access to data from experimental collaborators (Boxem Lab). In turn, your models will help our experimental
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. Examples of topics include algorithmic fairness in network analysis, developing network embedding frameworks for real-world network datasets or AI models based on agentic LLMs for simulating real-world
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electricity price signals, demand-response mechanisms, and time-of-use optimization. AI-Driven Optimization using Reinforcement Learning: Apply RL algorithms to develop and train agents that optimize power
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their property. Methodology: The aim is to apply a newly developed Coupled Human And Natural Systems (CHANS) model to simulate and understand the interactive human behaviours and social dynamics before and during
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PhD Position: Activating Heritage as a Mediator for Dialogue and Belonging in an Era of Polarization
sustainability in society and active agents shaping narratives By collaborating with heritage sites and the resources of cultural institutions, leveraging their collections, material culture, and local stories as