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specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids. This role involves creating advanced probabilistic models that capture the complex behaviors
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increasing independence in defining research problems, developing methodologies, and driving publications. The role values strong research judgment and analytical thinking, complemented by effective use
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, machine learning, and control in the energy sector. The postdoc researcher will perform theoretical study and algorithm development on optimization/control/data analytics methods and authorize peer-reviewed
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. The project will involve development of novel parallel algorithms to facilitate in-situ analyses at-scale for multi-million and multi-billion atom simulations. In this role, you can expect to work on enhancing
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to develop innovative technologies to improve the efficiency of resource utilization; to minimize our dependence on imported materials; and to enhance our national security. This position is broadly focused
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The Materials Science Division (MSD) at Argonne National Laboratory is seeking highly motivated applicants for a postdoctoral appointee to join a multidisciplinary team developing next-generation
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develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel solver, capable of running
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We are seeking a highly motivated Postdoctoral Appointee with a strong background in artificial intelligence and machine learning (AI/ML), with particular emphasis on the development and application
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modeling of x-ray spectroscopies sensitive to molecular chirality; simulations of x-ray–induced ultrafast electron-transfer, decay, and nuclear dynamics in gas- and liquid-phase systems; and the development
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environment Demonstrated ability to think independently and innovatively to develop creative solutions Strong organizational skills and attention to detail Ability to model Argonne’s core values of impact