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(e.g., Teuken 7B, Llama 3, Mistral 7B) specifically for physics and materials science applications scenarios as well as for related research data management tasks Developing LLM-driven agents
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focus on agent-based modeling and the quantitative analysis of spatial structures in experimental data. In doing so, you will develop your own research focus, support conceptual work, and contribute
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related field Excellent English skills are essential Strong interest in interdisciplinary work and team orientation Experience with one or more of the following: programming languages (C++, Python), agent
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“Equilibrium Learning, Uncertainty, and Dynamics.” About the Project Market interaction is increasingly automated by artificial learning agents. Examples include pricing agents in electronic retail or bidding
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telescopes, and AI-driven discovery of new physics experiments to test quantum-gravity and observe gravitational waves Agentic frameworks (e.g. LLMs with tool-use) for closed-loop idea generation for physics
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, equitable, trustworthy, and context-sensitive. Multi-agent architectures where multiple AI systems collaborate, negotiate, and adapt to model complex human learning processes and support group work
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labeled data and that the learned intelligent agents can perform better than humans. Instead of verifying the correctness of neural networks, we build a safety net that only forwards safe actions