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backscatter diffraction, focused ion beam specimen preparation, and computer vision or machine-learning analysis of microscopy datasets. The position requires strong experimental, analytical, written, oral
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Argonne National Laboratory to advance learning-enabled imaging methods. This position offers a unique opportunity for candidates with backgrounds in electrical engineering, computer science, applied
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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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environment that brings together computer scientists, AI researchers, domain scientists, software engineers, and high-performance computing experts. You will help design and implement new methods for
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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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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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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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, Microsoft Excel and other computer programming skills. Knowledge of process modeling and life cycle analysis. Knowledge of production processes for critical materials and minerals such as lithium, cobalt
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capable computers. The division aims to build lab-wide cross-cutting simulation application capabilities integrating with mathematics, computer science, domain science, and advanced computing architectures
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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