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selection criteria Experience with agentic AI systems, LLM-based agents, tool use, planning, or multi-agent systems. Knowledge of trustworthy AI topics such as interpretability, explainability, uncertainty
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, qualification, and deployment of AI agents and models, Computational Fluid Dynamics (CFD) simulation codes, and Finite Element Method (FEM) based tools for nuclear energy (fission and fusion) applications
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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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faculty based on University campuses, at Research and Extension Centers and in every Arkansas county. It consists of the Arkansas Agricultural Experiment Station (AES) and the Arkansas Cooperative Extension
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researchers across ETH accelerate their research with frontier AI. You will partner with research labs to understand their scientific challenges and co-develop agentic solutions: multi-step pipelines that plan
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the distributed hydrologic model ATS (The Advanced Terrestrial Simulator), coupled to a trait-based plant-soil-microbe model (ATS-EcoSIM). The code provides a framework for three-dimensional hydrologic
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eligibility criteria. Documented ability to work independently and autonomously. Experience in the development of distributed AI or multi-agent systems based on current research. A relevant publication record
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a US-based service which processes data on servers based outside the EEA. The Interfolio privacy policy can be viewed here www.elsevier.com/legal/privacy-policy . Information submitted via hard-copy
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optimization and meta-heuristics; economic paradigms (game theory, mechanism design, electronic markets); agent modeling and simulation; agent planning, scheduling and decision support; agent learning and
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optimization and meta-heuristics; economic paradigms (game theory, mechanism design, electronic markets); agent modeling and simulation; agent planning, scheduling and decision support; agent learning and