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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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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 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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synthesis and crystal structure analysis using XRD Rietveld analysis Knowledge of aqueous electrochemical techniques Experience in X-ray absorption spectroscopy (XAS), X-ray scattering (SAXS & WAXS), and X
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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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., transient absorption and emission), including laser operation, optical alignment, detector interfacing, and data analysis Excellent written and verbal communication skills Ability to model Argonne’s core
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diffraction, experience writing successful proposals for synchrotron experimental beam time at large scale facilities, x-ray data analysis expertise from single crystal materials. Knowledge of quantum 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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Knowledge, Skills, and Experience: Proficiency in mathematical analysis and operator theory. Experience working with microelectronics. Experience in conducting synchrotron experiments and analyzing
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modeling of crystals, dislocation dynamics, and defect analysis, linking atomic-scale simulations to macroscopic properties. Familiarity or interest in machine learning methods and computing frameworks