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methods, and multi-agent systems. Research topics may include efficient exploration, learning under uncertainty, online decision-making, scalable AI systems, and applications of modern AI in domains such
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orchestration; planning and memory; multi-agent collaboration. Scientific Reasoning: Program/path-of-thought, tool-augmented and retrieval-augmented reasoning; uncertainty quantification and calibrated
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novel methods to optimise de-ice ETIPS, which will replace current manual and slow processes. It is envisaged that the PhD research will develop a range of novel agent based AI/ML techniques and neural
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-Marc Panaud Bactérie Helicobacter pylori en microscopie électronique à balayage. Agent causal de pathologies de l'estomac : elle est responsable des gastrites chroniques, d'ulcères gastriques et
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University of Oslo and UiT The Arctic University of Norway | Norway, Michigan | United States | about 10 hours ago
Norway, Faculty of Science and Technology Starting date: preferably in 2026, by agreement Project 6: Multi-agent knowledge bases Employment: UiT - The Arctic University of Norway, Department of Physics and
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Processes. Coursework or project experience in multi-agent systems, reinforcement learning, and/or probability theory will strengthen your application. - "Self-adaptation" project. The goal is to design
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with County Extension Agent Liaisons to maintain daily contact with local elected officials during recovery operations. Serves as a surge force that deploys into the disaster theater for response as
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for: Designing a multi-agent orchestrator that can handle multiple datacenter sites. Implementing multi-timescale control, from sub-second to hourly decisions. Developing learning-based decision
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extraction attacks, designing protocols that detect and mitigate adversarial attempts to reconstruct model functionality or steal sequential reasoning paths during multi-agent interactions. Secure agentic
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such as production efficiency, energy consumption, operational cost, product quality, safety, and sustainability. This project investigates how multi-objective RL can learn effective control policies