92 data-"https:" "https:" "https:" "CMU Portugal Program FCT" Postdoctoral positions at Argonne
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scalability studies to identify and improve bottlenecks in large codes. Experience in development of data-driven reduced-order models in one or more of these areas: turbulence, boundary layer flows, combustion
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facilities with relevance to critical materials and nuclear reprocessing Analyze data, prepare manuscripts for submission to peer-reviewed publications, prepare technical reports for sponsors, and attend and
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optical and THz techniques. Ability to analyze and understand complex data set is required. Experience to lead ultrafast x-ray scattering or electron scattering experiments is a plus but not required
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or equivalent. Knowledge and experience with analytical techniques such as XRD and SEM. Skill in devising and performing experiments to acquire data, using and maintaining research equipment, compiling
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optimization schemes. From developing AI models to uncover structure-function relationships with limited data sets, to building automated electrode-electrolyte interface discovery workflows and implementing full
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technologies, and in advancing data-driven risk monitoring approaches for supply chain resilience. The candidate will assist with data collection, analysis, and scenario modeling for a DOE-sponsored assessment
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techniques to solve pressing challenges in energy storage. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne
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science for quantum information hardware with the industrially mature solid state platforms of silicon/silicon germanium and silicon carbide spin qubits. The position will focus on heterogeneous integration
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, MATLAB, or similar programming environments for instrument control and data analysis. Excellent written and oral communication skills. Demonstrated ability to work both independently and collaboratively in
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computational scientists to advance a next-generation, user-friendly, agentic AI platform for automated data analysis, interpretation, and user interactions. The appointment is expected to last two years and the