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The Time-Resolved Research Group in the X-ray Science Division at Argonne National Laboratory invites applications for a Postdoctoral Appointee. The role focuses on developing ultrafast pump–probe
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programming, interfacing hardware, and developing machine-learning methods highly desirable. The researcher will join an Argonne funded project with interdisciplinary team of material scientists, computer
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on the development of the SPHEREx Legacy Galaxy Clusters Catalog. The successful candidate will lead analyses to characterize galaxy populations in clusters using SPHEREx data in combination with complementary wide
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We are seeking a highly motivated and flexible postdoctoral researcher to join the Applied Materials Division (AMD) at Argonne National Laboratory to develop advanced methods for in situ and
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, and autonomous materials discovery. This position, supervised by Ashley Bielinski and Alex Martinson focuses on the development of semiconductor materials and the repair of electronic defects
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on developing a new hybrid light–matter platform that couples transition metal complexes with optical microcavities to achieve optical control over ultrafast spin conversion and charge transfer
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advanced synchrotron X-ray techniques. The successful candidate will be at the forefront of integrating cutting-edge AI methodologies with world-leading X-ray characterization. You will develop and apply
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laboratory partners, and contribute to the development of separation technologies for energy, water, and critical resources. Key Responsibilities: Develop and apply in-situ methods (e.g., optical coherence
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Separations Postdoctoral Research Associate will develop capacitive deionization systems for the selective recovery of critical materials and also investigate electrode aging, degradation, and durability
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specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids. This role involves creating advanced probabilistic models that capture the complex behaviors