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The High Energy Physics Division at Argonne National Laboratory (ANL) invites applications for a Postdoctoral Research Associate position to join our team working on the ATLAS experiment at the Large
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ideal for someone who enjoys working at the intersection of data science, machine learning, materials research, and experiment, and who is motivated to translate computational advances into real
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methods for active learning, optimization, inverse design, and experiment planning Build analysis tools for multimodal, high-throughput experimental data, including real-time or near-real-time
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available. Explore agentic AI approaches for federated learning, including AI agents that can assist with task orchestration, experiment planning, model evaluation, workflow automation, and decision
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responsibilities will be the development of AI models for robotic control and the demonstration of these methods via simulation and experiment. Beyond the listed projects, the candidate may contribute to
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are involved in SpinQuest at Fermilab and the MUSE experiment at PSI. Our hardware program includes the ePIC Barrel Imaging Calorimeter, and instrumentation R&D such as a polarized lithium-ion source
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Prototyping Lab: https://rpl.cels.anl.gov/ Autonomous Discovery at Argonne: https://www.anl.gov/autonomous-discovery MADSci on GitHub: https://github.com/AD-SDL/MADSci AD-SDL organization on GitHub
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unprecedented scale. For more information: Rapid Prototyping Lab: https://rpl.cels.anl.gov/ Autonomous Discovery at Argonne: https://www.anl.gov/autonomous-discovery MADSci on GitHub: https
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datasets, spanning the full experimental cycle from real-time X-ray measurements to post-experiment reconstruction: Develop learning-enabled algorithms for 3D reconstruction of noisy and heterogeneous
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documenting experiment progress, technique development, and new initiatives, providing valuable insights to peer reviewers and program managers alike. Position Requirements Required skills and