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Field
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to facilitate the management and editing of the different specialist agents that make up the system. - Implementing mechanisms to generate artificial conversations, based on historical case studies, for training
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be developing and applying advanced AI models and sophisticated multi-agent systems to address pressing challenges in regulatory genomics. Key research areas include: Mechanisms of Common and Rare
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; The prototype will analyze the student's interactions with the study material and based on this analysis, the agent can, for example, present the same information in different formats, or adjust the level of
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use of in vivo and in vitro inhalation exposure models to develop medical countermeasures against chemical threat agents. In addition, you will participate in research projects characterizing the toxic
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learning, including designing, developing and evaluating systems and models to enhance learning through AI technology. The Postdoc fellow will engage with developing AI-agents, models, frameworks and
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experience in software development. Experience applying large language models (LLMs) or autonomous agents to scientific tasks such as code generation, protocol reasoning, or automated experimental planning
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software development. Experience applying large language models (LLMs) or autonomous agents to scientific tasks such as code generation, protocol reasoning, or automated experimental planning. Proven ability
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offered by agents. Focus on personalized support for the student and the teacher in various phases of the learning process. Modeling and Exploration of Emotional and Cognitive Profiles of Agents:Develop
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to be developed consists of: Architecture of Collaborative Agent Ecosystems with Human Interaction:Explore and develop architectures where different AI agents (based on LLMs) play distinct roles, such as
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with NERSC staff, domain scientists, and partners at NVIDIA and Dell to prepare high-impact workflows for 12,000+ NERSC users. What You Will Do: Contribute to one or more NESAP AI-based scientific