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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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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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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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methods, network flow modelling and multi-agent or decision-simulation approaches, the student will assess trade-offs between local objectives and national-level resilience outcomes. A further strand will
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progressed capabilities towards exploiting zero-day vulnerabilities. Frontier models show promising performance when combined with a focused knowledge base and multi-agent architectures. However, in most cases