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electropolishing, flash polishing, and focused ion beam methods. Experience applying computer vision, image analysis, and/or machine-learning methods to microscopy or materials characterization data
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, Physics, or a related field. Strong expertise in machine learning, computational imaging, computer vision, or signal processing. Proficiency in scientific programming and modern ML frameworks, with
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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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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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closing this gap. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary
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-material production, and recycling; compile life-cycle inventories from scientific literature, technical reports, patents, public industrial information, supplier data, and other sources; translate
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information so that it is understandable to a broad audience. Strong computer skills relevant for data manipulation and analysis including experience with creating and using complex models in Simulink
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The Nanoscience and Technology Division at Argonne National Laboratory invites applications for an Assistant Scientist (RD2) position in the Nanofabrication and Devices Group within the Center 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