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We are seeking a highly motivated and creative Postdoctoral Researcher to join the X-ray Science Division (XSD) at Argonne National Laboratory. The successful candidate will develop web-based AI
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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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Postdoctoral Appointee - Investigation of Electrocatalytic Interfaces with Advanced X-ray Microscopy
part of the DOE–BES initiative Integrated Scientific Agentic AI for Catalysis (ISAAC) , a multi-facility collaboration integrating experimental modalities and simulations to enable an orchestrating
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workflows, and immersive or experimental interfaces Integrate LLM-based and agentic AI systems with scientific visualization frameworks, in situ pipelines, and data analysis workflows Prototype and evaluate
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implement pioneering agentic AI workflows for autonomous materials characterization. We are building the next generation of AI-powered laboratories, where intelligent agents can formulate hypotheses, run
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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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. Quantum Mechanical Calculations: - Performing first-principles based or Density Functional Theory (DFT) calculations for molecules/materials and interphases - Utilizing Molecular Dynamics (MD) simulations
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databases and retrieval-augmented generation (RAG) and integrate agentic AI systems to meet the demands of large-scale fine-tuning and inference. The postdoc will also work on design and implement agentic-AI
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-Informed Neural Networks (PINNs) and geometric deep learning. Experience with active learning, agentic workflows, or other methods for autonomous experimentation. Familiarity with high-performance computing
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, performance, and reliability for scientific applications. Develop and deploy autonomous LLM agents capable of reasoning, planning, and decision-making to support complex scientific workflows. Implement