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structure. This PhD project will investigate systemic financial risks emerging from the convergence of agentic artificial intelligence and retail trading platforms. Using a combination of experimental
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of lightweight, logic-based machine learning approaches. In addition, agents must support collective decision-making to achieve system-wide optimisation rather than isolated, local improvements. Finally
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and responsible artificial intelligence, combining areas such as reinforcement learning, social and cognitive computational modelling, knowledge representation and reasoning, agent-based modelling. More
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LLM Agents: Foundations, Attacks, and Defenses”. Your work assignments Large language model (LLM) agents represent the next generation of artificial intelligence (AI) sys- tems, integrating LLMs with
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organization Knowledge of at least one empirical method (desirable) - Computable general equilibrium modeling and simulation or - Econometrics or - Agent-based modeling Excellent command of English High personal
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. Applicants should have an interest in manufacturing systems, operations research, and simulation modelling. Candidates with prior experience in areas such as discrete-event simulation, agent-based modelling
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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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behaviours of multi-agent systems in response to changing internal states and external environmental conditions. Both traditional model-based approaches and modern learning-based control techniques will be
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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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. Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine. In CoRR, abs/2311.16452, 2023. [33] S. Lamsiyah, A. El Mahdaouy. A Reinforcement Learning-based Method