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magnetic thin films, patterned structures, and complex interfaces. In this advertised role, you will be conducting real-space imaging of magnetic heterostructures using LTEM to understand spin textures and
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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
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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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MEP Group Argonne National Laboratory, situated near Chicago, is a prominent multidisciplinary science and engineering research center. The Medium Energy Group in the Physics Division, comprises eight
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enzyme engineering workflows on their respective leadership computing platforms. Key Responsibilities: Design and implement generative AI models and agentic systems capable of scientific reasoning and
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-completed PhD (typically completed within the last 0-5 years) in chemical engineering, environmental engineering, or similar degree. Experience with data collection, processing, analysis, and presentation
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scientists and engineers are accustomed to. Moreover, the vast majority of the performance associated with these reduced precision formats resides on special hardware units such as tensor cores on NVIDIA GPUs
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group in the division, comprising 11 Ph.D. scientists and approximately 7 postdoctoral researchers. The group conducts world-leading research in nuclear structure, nuclear astrophysics, fundamental
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(CO2) conversion processes and contribute to engineering design of upscaled processes. The candidate will be a part of the Applied Materials Division (AMD) within AET at Argonne and will contribute
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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