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- XIAN JIAOTONG LIVERPOOL UNIVERSITY (XJTLU)
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agents for X-ray spectroscopy by integrating large language models (LLMs) with physics-aware spectroscopy workflows. The researcher will work closely with a multidisciplinary team of X-ray physicists and
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 2 hours ago
mission development as well as societal applications. The overarching goal of this project is to enhance ISSM’s capability, scalability, and accessibility by developing a multi-modular, agentic AI framework
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implement pioneering agentic AI workflows for autonomous materials characterization. We are building the next generation of AI-powered laboratories, where intelligent agents can formulate hypotheses, run
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east. For detailed information about the university, please visit www.xjtlu.edu.cn . ABOUT THE INDUSTRIAL INTELLIGENT AGENT RESEARCH CENTER The Industrial Intelligent Agent Research Center at Xi'an
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and Allocations”, as part of an interdisciplinary research effort at Royal Holloway, University of London. The postholder will help lead work to design and build artificial agents that reproduce human
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foundations of agentic reasoning, in close dialogue with complex real-world problems. Essential requirements: PhD (or near completion) in AI/ML/NLP, Computer Science, or related * Strong research track record
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description of the project] * Background of the recruitment and description of the project The Agentic AI Research Team (AART) is seeking an outstanding postdoctoral researcher specializing in NLP or applied
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holder is a member of the Systems Security Lab with responsibility for carrying out research on the topic of security of AI agents and related AI systems. The post holder provides guidance to junior
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Philanthropy and is fixed-term until 31 Dec 2027. A further extension to this would be based on funding availability. You will conduct hands-on empirical research in multi-agent security and agentic AI security
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systems Reinforcement Learning and Agentic Control: Hands-on experience with reinforcement learning, multi-agent systems, or planning-based agents for autonomous vehicles or robots operating in dynamic