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The Advanced Grid Modeling group at Argonne National Laboratory's Center for Energy, Environmental, and Economic Systems Analysis (CEEESA) is seeking a highly motivated Postdoctoral Researcher
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The Hydrogen and Fuel Cell Materials Group in Argonne National Laboratory’s Chemical Sciences and Engineering Division is seeking to hire a Postdoctoral Appointee to participate in a project that
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position to develop and apply advanced analysis methods, including artificial intelligence and machine learning algorithms and approaches, for x-ray science and instruments. These methods will accelerate
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The Chemical Sciences and Engineering Division is seeking a highly qualified and motivated postdoctoral researcher to join our team in the area of light-matter interactions, with a particular focus
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microscope, as well as electrostatic beam blanker or ultrafast pulser in electron microscopes. Proficient in data analysis and modeling, with experience using Python and other programming or simulation tools
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the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis
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The Advanced Grid Modeling group at Argonne National Laboratory's Center for Energy, Environmental, and Economic Systems Analysis is seeking a dedicated Postdoctoral Researcher. This role is ideal
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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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defect centers. This project aims to explore the use in-situ x-ray diffraction techniques available at the newly-upgraded Advanced Photon Source synchrotron at Argonne as a means to gain new insights
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. The second aim of the project will use the digital twin to develop experimental methods and algorithms to enable robust phase retrieval of reciprocal space measurements to direct space images. These methods