79 structural-engineering-"https:"-"https:"-"https:"-"https:"-"https:" positions at Argonne
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Argonne’s Nanoscience and Technology Division seeks a postdoctoral scientist to advance transmission electron microscopy (TEM) studies of materials and interfaces relevant to microelectronics
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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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, engineering, computational science, a physical science (materials science, chemistry, physics etc.), or related field. Hands-on experience with AI frameworks and employing large language models. Strong Python
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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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. The successful candidate will be a key contributor to a multidisciplinary co-design team spanning material science, computing, and electronic engineering, with the goal of enabling next-generation detector
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total X-ray scattering (TXS) and pair distribution function (PDF) analysis capabilities and methodology to study laser-driven structural dynamics in functional materials. This position is part of a
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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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Electrochemical Mass Spectroscopy (DEMS), and complementary methods Perform electrochemical testing and benchmarking; analyze and interpret complex datasets to elucidate catalytic mechanisms and structure–property
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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