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The High Energy Physics Division at Argonne National Laboratory invites applications for a postdoctoral appointment focused on the design and simulation of advanced detectors for future high-energy
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design and build custom, non-commercial apparatus compatible with synchrotron scattering and imaging techniques at the Advanced Photon Source. Candidates with prior experience in developing operando
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
vulnerabilities. The Postdoctoral Appointee will be responsible for the conceptual framework, design, and implementation of these models, ensuring scalability on the DOE’s leadership computing facilities. Position
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validation datasets Integrate surrogate models into workflows to predict bias-driven structural and electronic evolution Design and execute high-throughput calculations; build and manage curated materials
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leaching, solvent extraction, ion exchange, electrodialysis, membrane separation, and crystallization or precipitation. Position Requirements Recent or soon-to-be-completed PhD (typically completed within
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, designing and executing ML experiments on leadership-class computing facilities such as the Aurora and Polaris supercomputers. Argonne is a multidisciplinary national laboratory and offers an exciting campus
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to assess evolving risks in coastal-urban regions. Other key responsibilities include: Mesh design and high-resolution data utilization. Develop and refine high-resolution barotropic ocean meshes along U.S
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samples with significantly higher temporal resolution than traditional scanning methods. The selected candidate will simulate and design the experimental setup, and then perform single-frame ptychography
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by January 16th, 2026, given full consideration. The position will remain open until filled. Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in Astronomy
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foundational models to describe IDP interactions under various physiological conditions, both normal and cancer related Use these models to iteratively design, validate, and refine experiments, leading