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, benchmarking and deploy them for applications. The ideal candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning
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, conferences, symposia, and seminars. Position Requirements A formal education in materials science and engineering, nuclear engineering, or a related field at the PhD level with zero to five years of
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is extended yearly. Position Requirements A recent PhD (within 5 years) in computational chemistry, chemistry, materials science, physics, computational science, computer science, engineering, or a
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0-5 years in computer science, mechanical engineering, artificial intelligence, or related engineering disciplines Experience with state of the art AI methods for robotics Experience with robotics
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branches of physical sciences, or in math, computer science, and electric engineering who have an interest in accelerator physics will also be considered. Strong programming skills. Proficiency in the
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range of core scientific capabilities, from nuclear physics and materials science to biology and advanced computer science. The Physics Division (PHY) at Argonne has a long and distinguished history in
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engineering, materials science, polymer science, physics, computer science, and/or data science Demonstrated accomplishments in materials informatics, scientific machine learning, or AI-guided
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research group and with scientists from a variety of scientific backgrounds, such as microbiology, chemistry, x-ray physics, hydrology, geochemistry, soil science, and computer modeling. In this role you
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with computational scientists in other divisions, including Mathematics and Computer Science (MCS) and the Argonne Leadership Computing Facility (ALCF). The group has significant involvement in three