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. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary responsibilities will be
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to analytical techniques for characterizing effluent analytes using LC-MS, GC-MS, IC, ICP-MS, including developing in situ measurement routines aimed at understanding degradation mechanisms of membrane and
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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
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or soon-to-be-completed PhD (typically completed within the last 0-5 years) in physics, chemistry, or materials science with 0 to 2 years of experience, or the equivalent experience through practical
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Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in physics or a closely related field Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork
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models Disseminate research through publications, presentations, and open-source contribution Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in Materials Science, Data
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, experience in scaleup is a plus. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in chemistry and/or closely related discipline. Expertise in the study
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with this group to evaluate AERIS at S2S scales, couple ocean component to the model, data assimilation and regional refinement. In particular, this position will utilize generative AI to create a
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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