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Field
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molecular drivers. Develop AI agents and LLM-based computational workflows for biomedical research, including automated dataset discovery, quality assessment, multi-omics analysis, biological interpretation
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foundation models, knowledge graph/ontology, federated learning or collaborative agents, AI security, etc.; (c) have experience in research proposal development; (d) have strong publication records in
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verified reference monitor on top of seL4 for the secure containment of AI agents. We will develop new mechanisms for dynamically controlling agents’ capabilities and information flows, together with machine
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verified reference monitor on top of seL4 for the secure containment of AI agents. We will develop new mechanisms for dynamically controlling agents’ capabilities and information flows, together with machine
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methods for reasoning over multi-agent interactions. This Research Assistant position will contribute to research exploring how temporal knowledge representation, abductive and probabilistic reasoning and
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Systems Lab (https://psl-ntu.github.io ) at NTU’s CCDS. The role focuses on designing, developing, and evaluating efficient, scalable systems for modern multi-agent HPC-AI workflows. Responsibilities
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simulations that can aid in policy design. The Research Assistant will be work closely with the Principal Investigator and other group members on this project. Key Responsibilities Develop LLM-based agent
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agents for data analysis, and the design of interactive systems that keep human analysts in control of AI-assisted sensemaking. Responsibilities Conduct research on agentic visualization: generative AI
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that can aid in policy design. The Research Fellow will work closely with the Principal Investigator and other project members on different aspects of the project including persona creation, agent
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how low Earth orbit (LEO) satellites can operate as intelligent agents, learning to make decisions and coordinate their actions as orbital and traffic conditions change. Your research will combine