81 structural-engineering "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" Postdoctoral research jobs at Argonne
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the last 0-5 years) in geology, earth sciences, chemistry, chemical engineering, or materials engineering (those with other degrees but have similar skills to those listed will be considered). Experience in
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-be completed (typically within the last 0-5 years ) Ph.D. in engineering, operations research, computer science, applied mathematics, or a related field. Demonstrated expertise in mathematical
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, atomic physics, optical physics, electrical engineering, materials science, or a related field Experience in photonic and/or superconducting device nanofabrication Experience working with lasers and
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magnetic thin films, patterned structures, and complex interfaces. In this advertised role, you will be conducting real-space imaging of magnetic heterostructures using LTEM to understand spin textures and
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science. Position Requirements Ph.D. (completed or soon to be completed prior to the start of the appointment) in Physics, Materials Science and Engineering, Electrical Engineering, or a closely related
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We are seeking a highly motivated postdoctoral researcher to conduct independent research on foundation models for scientific and engineering applications, with an emphasis on training, adaptation
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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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to contribute to other large-team scientific projects in materials engineering, chemistry, and beyond at Argonne National Laboratory. Position Requirements Required skills: Recently completed PhD (within the last
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pressure diamond anvil cell technology. Excellent oral and written communication skills. Ability to model Argonne's Core Values: Impact, Safety, Respect, Integrity, and Teamwork. Preferred Knowledge, Skills
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scientists and engineers are accustomed to. Moreover, the vast majority of the performance associated with these reduced precision formats resides on special hardware units such as tensor cores on NVIDIA GPUs