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of foundational and agentic AI models (LLMs, generative AI, RAG-based architectures) for autonomous, context-aware decision-making. Researching and implementing trustworthy human-machine interaction methods
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-critical scenarios. Support the implementation of agent-safety RL training for SWE-agent-like coding agents using base models from the Qwen3.5 family. Assist in building and maintaining controlled sandbox
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on a single monolithic model, multiple specialised AI agents can collaborate to analyse patient data, retrieve supporting medical evidence, estimate uncertainty, verify each other's conclusions, and
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agents to improve based on experimental feedback. You'll translate wet-lab and computational endpoints into a trainable signal to build models that plan and act against them. Responsibilities: Research
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when using closed, proprietary models, where model weights, training data, and internal representations are inaccessible. The PhD project will therefore investigate how trustworthy agentic AI systems can
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, data leakage, RAG poisoning, multi-turn attacks. Review LLM system architectures (agentic, multimodal, RAG) for security vulnerabilities and provide design recommendations. Document vulnerabilities with
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analytics, or network analysis. • Experience in agent-based modeling or simulation is highly desirable. • Demonstrated ability to publish in internationally recognized peer-reviewed journals
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large language model and agentic systems, including multi-agent orchestration frameworks, tool and protocol integration standards, self-hosted and API-based inference, evaluation, and prompt engineering
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solutions, not just infrastructure — someone who sees a research challenge and immediately starts thinking about what combination of agents, models, and pipelines could solve it. Part of the work is carried
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that support research and operational workflows across the Office of Data Science. This role will focus on building and orchestrating advanced LLM-based agents, developing secure and interoperable infrastructure