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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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, study, design, development and validation of advanced artificial intelligence solutions for the implementation of agentic systems based on LLMs, with particular reference to orchestration techniques and
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and experimentation in the area of Multi-agent Agentic AI systems applied to 6G network and service management. By leveraging recent advances in Large Language Models (LLM) and other key agentic tools
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learning or multi-agent systems. Experience with cloud-native technologies (Docker, Kubernetes) or distributed computing. Experience with efficient neural architectures, scalable model design, or resource
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. The project will conduct leading research and experimentation in the area of Multi-agent Agentic AI systems applied to 6G network and service management. By leveraging recent advances in Large Language Models
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; _Foundations in artificial intelligence (symbolic AI, machine learning, multi‑agent systems, or data analysis); _Interest in governance, safety, trust, and validation of AI‑based systems; _Programming skills and
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multi-agent systems, sensor fusion, or autonomous systems is a plus Ability to work in a multidisciplinary environment combining robotics, control, and wireless sensing Good scientific writing and
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architectures or autonomous systems. Familiarity with agentic AI concepts such as autonomous agents, multi-agent systems, tool use, and orchestration. Hands-on experience with agentic frameworks such as LangChain
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, LangGraph). Experience building agentic LLM systems with tool-calling, multi-step reasoning, or autonomous workflow orchestration. Experience with retrieval-augmented generation (RAG), vector databases
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- Mobile or maritime robotics, automation, or autonomous systems. - Dynamic modeling and physical simulation. - Trajectory planning, navigation, or robot control. - Interest in multi-agent systems and human