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beam (FIB) is preferred Programming proficiency (e.g., Python) for experiment automation and/or image analysis is preferred Background in electronic, magnetic, or optical materials is preferred
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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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generalization to support robust analysis, interpretation, and decision-making. Apply conformal prediction and uncertainty quantification techniques to generate reliable confidence estimates and risk assessments
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, etc.) and quantitative data analysis. Excellent written and oral communication skills. Ability to work effectively in a collaborative, multi-institutional team environment. Ability to model Argonne’s
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experimental electronics and timing systems (e.g., pulse generators, delay generators, gating electronics, and synchronization circuits). • Demonstrated proficiency in x-ray data analysis and instrument control
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cells and electrolyzers is welcomed. Experience with statistical analysis methods such as PLS-DA, supervised learning and database building are highly encouraged. The applicant is expected to think and
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total X-ray scattering (TXS) and pair distribution function (PDF) analysis capabilities and methodology to study laser-driven structural dynamics in functional materials. This position is part of a
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, distributions, and dynamics in metallic, oxide, and semiconducting systems. This project integrates high-throughput and in situ TEM experimentation with AI/ML-driven image analysis and computational modeling
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We are seeking a highly motivated Post-Doctoral Researcher to develop, implement, and advance the IDeA (Intelligent Design and Analysis) co-scientist project. The successful candidates will work
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of molten salt chemistry and electrochemistry Develop novel process monitoring and control technologies applicable to molten salt and liquid metal systems Develop advanced molten salt flow systems to enable