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: Proficiency in machine learning, statistical modeling, and quantitative methods for multi-omics data analysis Molecular Simulations: Expertise with molecular simulation tools like OpenMM, AMBER, Gromacs, and
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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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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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thin film deposition is preferred. Advanced image processing and analysis skills. Experience with micromagnetic simulation is preferred. Ability to work independently as well as in collaboration with a
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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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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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expected hiring range for this position is $148,125.00 - $231,075.00. Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based
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ideal for someone who enjoys working at the intersection of data science, machine learning, materials research, and experiment, and who is motivated to translate computational advances into real
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field Strong background in materials research for quantum information or sensing, preferably with practical experience. Proficiency in materials synthesis and fabrication, particularly with a focus on
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. This position will contribute to the Forge project, which is focused on developing advanced multimodal AI capabilities that can learn across distributed data environments without requiring sensitive data to be