66 data-"https:"-"https:"-"https:"-"https:"-"Paris-Saclay-university" Postdoctoral positions at Argonne
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computational science expertise. The ALCF has an opening for a postdoctoral position in data management targeting AI applications at scale. The successful candidate will join the AL/ML group, a vibrant
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The ALCF has an opening for a postdoctoral position in AI-assisted scientific visualization and intelligent data exploration. The successful candidate will join the Visualization and Data Analytics
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simulation, TEA and LCA, and have a good knowledge of current and future resource recovery and separation technologies. The successful candidate will 1) collect data pertaining to battery recycling, battery
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for candidates interested in the intersection of complex oxide epitaxy, quantum information, and nanophotonic to contribute to high-impact science at a national user facility. Key Responsibilities Develop and
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Postdoctoral Appointee - Investigation of Electrocatalytic Interfaces with Advanced X-ray Microscopy
to the ISAAC data repository by generating AI-ready physical descriptors and advancing data-driven understanding of dynamic catalytic processes. Responsibilities include : Identifying relevant user systems and
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and heterointerfaces. The postdoc will lead experimental design, data acquisition, and quantitative reconstruction. The appointees will work within a highly collaborative team spanning multiple DOE user
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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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design, development of supervisory control logic, validation of vehicle models against test data etc. The projects and interests of this group span all modes of transportation including off-road, rail
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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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the ability and motivation to develop expertise in large-scale model training and scaling on HPC systems, as well as in handling the unique characteristics of scientific data, including large-scale numerical