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multi-agent decision making in contested and dynamic environments, involving extensively applied work built on a strong mathematical foundation at the intersection of data science, data fusion, decision
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in analytical tools (e.g., MATLAB, Python, GIS, or similar). Knowledge of AI and Multi-Agent Systems. Proven ability to analyse complex information and summarise appropriately Ability to demonstrate
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the sophisticated AI engine (LLM fine-tuning, RAG system development, and integration of agentic capabilities). Skills gained The student will develop skills in research methodology, gaining hands-on experience
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