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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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FLAME-GPU accelerated agent-based modelling of material response to environmental and operational loading EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce
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, automation, or autonomous systems. - Dynamic modeling and physical simulation. - Trajectory planning, navigation, or robot control. - Interest in multi-agent systems and human-robot interaction. Technical
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interactions can be generated based on emerging and pre-defined protocols for interaction between agents, between agents and their environment and between agents and humans. By an interaction protocol, we mean a
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. Proven research track record demonstrated by publications in top-tier conferences. Demonstrated expertise in large language models (LLMs) and their application to software security, multi-agent system
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compositions of these materials with potential adsorption properties for simulants of toxic agents or catalytic properties of interest for degradation action, a molecular modeling approach based on density
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, or performance. In this project, the research done by the PhD student will address the interaction layer in a multi-agent systems. It will focus on how to secure interactions can be generated based on emerging and
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Essential & Desirable Criteria Essential PhD in relevant discipline (computer modelling of logistics or transport, agent-based modelling, sustainable logistics, or energy transitions) or equivalent industry
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atmospheric plasma to treat fibrotic diseases. The agent will join the emerging Innovative Cancer Treatment team. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR8175-LAUFOU-002/Default.aspx
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 11 hours ago
tailored to scientific modeling workflows. Rather than deploying AI as a black-box predictor, we propose a physics-aware AI agent architecture that interfaces directly with ISSM’s modular components