79 structural-engineering-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Dip" 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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The Argonne Leadership Computing Facility’s (ALCF) mission is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing
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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