62 computer-programmer-"Diamond-Light-Source"-"Diamond-Light-Source" positions at Argonne
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, distributions, and dynamics in metallic, oxide, and semiconducting systems. This project integrates high-throughput and in situ TEM experimentation with AI/ML-driven image analysis and computational modeling
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lead efforts to develop experimental techniques using conventional and coherent imaging in the ultrafast time domain, as well as a computational framework for modeling and reconstructing images
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methodologies and tools for economic and ecological analyses of hydropower systems. The position will involve the development and use of computer models, simulations, algorithms, databases, economic models, and
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computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced
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The Computational Science Division (CPS) at Argonne National Laboratory (near Chicago, USA) is seeking a postdoctoral researcher to enable exascale atomistic simulations of ferroelectric devices
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advanced computing, optimization, and data analytics technologies. The postdoctoral researcher will work with a team of researchers on solving challenging problems using optimization, stochastic models
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The successful candidate will be highly motivated and have a strong track record in problem solving and scientific publications. The candidate will be expected to conceive of, plan, and implement scientific
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information science and light–matter engineering, while engaging with CNM’s cleanroom and characterization capabilities, APS ultrafast and nanoprobe X-ray beamlines, MSD’s THz initiatives, and Q-NEXT’s national quantum
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electrocatalyst materials Plan and execute in situ/operando studies using advanced techniques such as X-ray Absorption Spectroscopy (XAS), X-ray Photoelectron Spectroscopy (XPS), Raman spectroscopy, Differential
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experience in economic and supply chain analysis, computational modeling, or policy analysis. Proficiency in scientific programming languages (e.g., Python, R) and data analysis libraries (e.g., pandas, NumPy