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of Large Language Models (LLMs) for scientific use cases. This position focuses on advancing LLM capabilities to address complex challenges across a range of scientific domains. As part of a
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computational models for industrial capacity planning, logistics optimization, material flow analysis, and supply chain analysis. Apply artificial intelligence, machine learning, LLMs, and advanced statistical
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and integrate emerging AI techniques (e.g., agentic workflows, LLMs) into scientific problems, ensuring methods effectively solve real domain challenges. Advanced Model Development: Design and debug
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agents for X-ray spectroscopy by integrating large language models (LLMs) with physics-aware spectroscopy workflows. The researcher will work closely with a multidisciplinary team of X-ray physicists and
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on the LLM/agent and HCI layers of IDEAS, including: Mixed-initiative AI assistants for visualization and analysis, Natural interaction paradigms for scientific workflows, Integration of agentic systems with
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data Familiarity with agentic LLM-based approaches and related technologies (e.g., RAG, MCP, A2A) Interest in interfacial phenomena and defect dynamics in materials across scales Job Family Postdoctoral
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, PyTorch, JAX etc. Experience with modern AI concepts such as large language models (LLMs), vision-language models (VLMs), model context protocol (MCPs), and the development of agentic AI tools. Skill in