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Field
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the creation of realistic simulated environments based on environmental data. - Defining evaluation protocols and performance metrics (safety, energy, mission efficiency). - Contributing to scientific validation
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period: six months] Duties The appointees will assist the project leader in the project - “Developing an LLM-based multi-agent language learning platform: Personalized tutoring and contextual simulation
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of cell factory engineering. Develop software tools that enable programmatic interaction between large language model agents and metabolic models, enabling automated simulation, interpretation, and
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environmental simulations in game engines and integrate them into a persistent multi-user environment platform. Develop and refine RAG-based AI agents using Google AI Studio Pro. Manage the workflow of converting
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(Lua/Java), agent behavior modeling, event handling, and API-based integration with external AI systems. Experience with distributed systems, reinforcement learning, or simulation environments (e.g
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, and can analogous mechanisms be engineered into multi-agent AI systems? You would answer this question by building and testing computational models, developing multi-agent simulations where agents
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. This position is part of the DOE-BES initiative Integrated Scientific Agentic AI for Catalysis (ISAAC), a multi-facility collaboration integrating experimental measurements, simulations, and data science to
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quantitative and qualitative research to develop empirically supported game-theoretic and agent-based models Apply econometric and model simulation methods to analyze governance mechanisms and to study the
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teaming strategies. - Participating in the creation of realistic simulated environments based on environmental data. - Defining evaluation protocols and performance metrics (safety, energy, mission
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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