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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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camera technology and multiplexed readout systems for quantum information science applications. In this role, you will join a multidisciplinary team spanning several Argonne divisions and contribute to
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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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prototypes, experimental benchmarks, and reusable software components. Position Requirements Required Skills and Qualifications: Ph.D. completed within the last 0–5 years in computer science, data science
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Cloët ( [email protected] ). About Argonne and the Physics Division: Argonne is a multidisciplinary science and engineering research center, where talented scientists and engineers work together to answer
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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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, 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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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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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