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(baseline 6-element prototype at Sector 25 to ~18 analyzer elements), mechanical/optical layouts, tolerances, alignment strategy, and stability requirements, detector, motion, and sample environment
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preparation of manuscripts, reports, and presentations; present findings at internal meetings and external conference Collaborate in a multidisciplinary team environment spanning experimentalists, theorists
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, co-simulation, or hardware-relevant environments. Primary Responsibilities: Develop machine learning and AI methods for control, optimization, and cyber-resilient operation of distribution systems, DER
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modeling and techno-economic assessment as appropriate. The candidate will work independently under general guidance, collaborate effectively within a multidisciplinary team environment, and prepare
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development and web-based applications, back-end services and API design (e.g., FastAPI, Flask), and deploying applications in local or cloud environments. Experience working with large-scale datasets
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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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deposition methods and equipment. Skills working interactively and productively in a multidisciplinary environment. Skills in oral and written communication. Record of publication and external recognition in
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environmental trade-offs. Contribute to projects involving capacity expansion, production cost modeling, and equilibrium modeling of power systems. Design and apply mathematical optimization models, including
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; present findings internally and at external conferences; contribute to publications Collaboration Environment Work in close coordination with ML development teams across Argonne (including the Advanced
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(HPC) environments and large-scale data management. Experience with version control (e.g., Git) and collaborative software development practices. Experience with real-time analysis and instrument control