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-agent systems. Methodology: Integrate ToM models with Reinforcement Learning based frameworks for single-agent and multi-agent decision-making. Develop simulation environments capturing realistic human
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The Computer Vision Group is looking for an aspiring PhD to investigate multi-agentic AI, LLMs, and VLMs applied to agricultural sciences. Currently, established AI models often fail to generalize
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, Mathematics, Computer Science, or a related quantitative field Have strong modelling, computational, and code development skills Have experience with network science, multi-agent systems, statistical mechanics
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platforms such as Unity, Unreal, or Godot Up-to-date knowledge of recent advances in generative AI, including vision-language models (VLMs) and agent-based AI systems Ability to design and conduct experiments
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curation. AI Safety: Ensuring robust alignment and safety in multi-agent LLM systems Efficiency: Streamlining large-scale model experimentation and training. Science of Deep Learning: Exploring mechanistic
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of the cytotoxic drug to kill tumour cells will be included to simulate the response of the tumour to ADC treatment. Stochastic or agent-based approaches will be used to describe the heterogeneity of antigen
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goals, generate task plans, and work safely and effectively alongside human operators in industrial settings. Recent advances in large language models (LLMs) and agent-based AI systems offer unprecedented
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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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framework integrating physics-informed machine learning, scenario generation, and human-in-the-loop preference-based reinforcement learning to prioritise climate-robust and equity-aligned interventions
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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