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The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. The Faculty of Science, Technology and Medicine (FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics,...
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-driven, knowledge-based agentic systems for automated alert triage and investigation in SOC environments, contributing to the AutoSOC platform Build explainable AI models that integrate planning, memory
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evaluation of the project’s core technical components in expert-in-the-loop agentic AI for manufacturing. Develop process-centric AI methods and software tools for BPMN modelling, Petri/CPN formalisation
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project – “Urban smart digital services via LLM-based multi-agent collaboration”. They will be required to: (a) develop multi-agent systems; (b) conduct experimental result analysis, paper and report
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will play a key role in building a parallelized, agent-driven exploration system and integrating a multimodal detection pipeline, ensuring real-time performance, scalability, and deployment readiness in
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The post The Faculty wishes to recruit a Postdoctoral Research Associate (Grade E) or Fellow (Grade F) to participate in the project “Agents that can err: Building the foundations for autonomous AI
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. We are looking for a Research Fellow to advance cutting-edge research in multi-agent systems for large language models (LLMs). The role will focus on conducting innovative research in multi-agent
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research ecosystem. Key Responsibilities: Review recent developments in the field of Large Language Models, agents, and world models for sustainability applications Design and develop AI or AI-enabled
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, robustness, and trustworthiness of multimodal AI agents. The role will focus on identifying novel attack vectors, developing automated red-teaming frameworks, and designing defense mechanisms to ensure
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develop foundational security theory for agentic and multi-agent AI systems, as part of our research programme in multi-agent security. You will build formal models of security-relevant behaviour in