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, synthesize, and process information to develop high-quality datasets and derive empirically driven results; work with other team members to develop and apply cutting-edge methodologies to address critical
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dynamics (CFD) to develop and optimize new processes and equipment designs using high-performance computing Analyze data, prepare manuscripts for submission to peer-reviewed publications, prepare technical
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modeling tools to develop and optimize new processes and equipment designs using high-performance computing Analyze data, prepare manuscripts for submission to peer-reviewed publications, prepare technical
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deployment, monitoring, and optimization of complex scientific data streaming workflows for current and future production infrastructures. The project will involve close collaboration with a team of systems
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-aware multi-modal deep learning (DL) methods. At Argonne, we are developing physics-aware DL models for scientific data analysis, autonomous experiments and instrument tuning. By incorporating prior
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at Materials Engineering Research Facility (MERF) and collaborators inside and outside Argonne. The candidate is expected to design and conduct experiments, analyze data and explore mechanisms behind
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techniques to enable multimodal online monitoring of chemical and radiochemical separations processes Acquire fundamental data relevant to chemical separations in support of related modeling efforts Analyze
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time The expected hiring range for this position is $70,758.00-$117,925.00. Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be
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, Safety, Health, and Quality Assurance standards and policies Process and interpret experimental data; prepare manuscripts for peer-reviewed publications Present research findings at scientific conferences
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performs world-leading research in nuclear structure, nuclear astrophysics, fundamental symmetries, and nuclear data. The Group also manages and operates world-class detector systems as part of the ATLAS