66 web-programmer-developer-"https:"-"https:"-"https:"-"U.S" Postdoctoral positions at Argonne in United States
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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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on understanding novel and emergent behavior in nanoscale magnetic heterostructures, particularly in confined 2D van der Waals magnets and related devices. The goal of the program is to study and control magnetic
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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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. Working within an interdisciplinary team, you will develop frameworks that connect atomistic features, mesoscale dynamics, and device-level performance. The effort will integrate heterogeneous data from
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The Applied Materials Division at Argonne National Laboratory has an immediate opening for a Postdoctoral Appointee. The candidate will be responsible for reviewing and developing design methods and
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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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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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for improvement, and/or analyze their impacts at individual, regional and national level. The candidate will also be tasked to develop new ideas into projects under the supervision of more experienced researchers
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on developing machine-learning surrogates for electronic structure and electrostatic potential and using these models to predict structural and electronic evolution under applied bias. Methods may include density