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(APS). The GL also contributes to the development of next-generation microscopy methods, instrumentation, and data workflows, while overseeing the group’s day-to-day operations, budget, user science
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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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data analysis, documentation, and scientific communication skills, including preparation of manuscripts, reports, and presentations. Ability to model Argonne’s Core Values: Impact, Safety, Respect
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will receive full consideration. Key Responsibilities AI-ready data and analysis for the ePIC Barrel Imaging Calorimeter and our Jefferson Lab program Support for the PRad-II and X17 experiments
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design, advanced modeling and high-performance computing, mathematics and data analytics, AI/ML algorithm development, and accelerator operations Ability to model Argonne’s core values of impact, safety
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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory seeks an outstanding postdoctoral researcher to advance data-driven, physics-informed AI for microelectronics materials
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Type Full time The expected hiring range for this position is $72,879.00-$121,465.00. Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will
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Because of the drastically increasing demand from AI/ML applications, the computing hardware industry has gravitated towards data formats narrower than the IEEE double format that most computational
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optical transition and favorable spin properties of individual solid-date erbium ions (Er3+) to store quantum information necessary for practical, robust, and scalable quantum communication. The focus
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. The candidate is expected to lead an effort to prepare generalized ML techniques for data quality monitoring for tasks across multiple HEP experiments. Experiments with Argonne involvement include, but are not