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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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analysis of experimental data, and the presentation of results at scientific conferences and in the peer-reviewed literature. Position Requirements This level of knowledge is typically achieved through a
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cases. This position is focused on advancing the capabilities of LLMs to address complex problems within specific scientific domains, with an emphasis on climate risk assessment and analysis. As part of a
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. The postdoctoral appointee will join a dynamic, multidisciplinary, and multi-institutional team. The postdoctoral appointee is expected to aid in the design, analysis, and interpretation of experiments as
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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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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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mechanisms through reaction kinetics analysis and other physical organic and inorganic techniques. Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in fields of organic
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preferred. Strong data analysis skills and ability in programming languages, such as Python, for performing experiments and analyzing data. Knowledge of innovative phase retrieval algorithms is desirable