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analysis frameworks that enable rapid interpretation of scattering measurements and facilitate the training of intelligent agents capable of guiding experiments and simulations in catalytic materials
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., specific code you wrote, modules you debugged, or workflows you designed). Highlight Transferable Skills: If your background is in a specific science domain (e.g., Physics, Biology), frame your experience in
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development and web-based applications, back-end services and API design (e.g., FastAPI, Flask), and deploying applications in local or cloud environments. Experience working with large-scale datasets
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early “pathfinder” experiments that establish performance and scientific impact. This role combines end-to-end instrumentation leadership (requirements, design maturation, interfaces, test/acceptance
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Plan and execute in situ/operando experiments using advanced characterization methods, including Near Ambient Pressure X-ray Photoelectron Spectroscopy (NAP-XPS), electron microscopy, Raman spectroscopy
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Postdoctoral Appointee - Investigation of Electrocatalytic Interfaces with Advanced X-ray Microscopy
potential experiments for CO₂RR, OER, and related electrochemical studies. Designing and performing XFM and complementary X-ray experiments at APS beamlines through user collaborations. Commissioning and
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simulations and experiments across scientific user facilities, leveraging data to understand complex material phenomena across scales. Key Responsibilities Design, implement, and validate physics-informed AI/ML
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and heterointerfaces. The postdoc will lead experimental design, data acquisition, and quantitative reconstruction. The appointees will work within a highly collaborative team spanning multiple DOE user
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The Argonne Leadership Computing Facility’s (ALCF) mission is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing
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relationships in next-generation electronic materials. This role involves creating AI models for real-time data analysis, enabling autonomous experiments through active learning and "curiosity-driven" exploration