39 evolution-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL"-"UCL"-"UCL" Postdoctoral positions at Argonne in United States
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Argonne National Laboratory’s Accelerator Science Division is seeking a Postdoctoral Appointee to contribute to the development of a Sub- THz Collinear Structural Wakefield Accelerator
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We are seeking a highly motivated postdoctoral researcher to join the Center for Nanoscale Materials (CNM) at Argonne National Laboratory. The successful candidate will contribute to the development
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of experimental quantum communication hardware development, optical memory qubit characterization, and fiber-based networking demonstrations using novel memory qubits. The goal is to employ the natural telecom
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artifacts, and developing an independent research agenda in AI for science. Core responsibilities include: Leading research on foundation models, including problem formulation, algorithmic development, and
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define requirements and performance specifications for future HEP/NP detector systems Perform detector concept development, system-level design, and optimization leveraging emerging computing architectures
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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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. 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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may include work at Jefferson Lab, the Electron-Ion Collider (EIC) program, detector research and development, and applications of AI in nuclear physics. Applications received by Tuesday, November 4
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photonic platforms through nano- and meso-scale lithographic fabrication. This position supports two complementary, three-year Laboratory Directed Research and Development (LDRD) projects focused on hybrid
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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