100 computer-science-intern "https:" "https:" "https:" "https:" "UCL" "UCL" positions at Argonne
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The Applied Materials Division (AMD) at Argonne National Laboratory is looking to hire a Postdoctoral Appointee – Materials Science. The Applied Materials Division conducts applied research
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candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning-based models for large dynamical systems (e.g. weather
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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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. This position offers an exciting opportunity to contribute to fundamental and applied research in materials chemistry using advanced computational techniques and artificial intelligence. The project involves: 1
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in computational science, machine learning, and experience with synchrotron data analysis are strongly encouraged to apply. Position Requirements PhD completed in the past 5 years or soon to be
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”, “Firstname_Lastname_cover_letter”. Include links to code examples in your CV (e.g., GitHub page, past project repositories). Position Requirements A recent PhD (completed within 5 years, or soon to be completed) in computer science
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effort at Argonne includes staff members from CPAC, the Computational Science division, and the HEP Detector group. It includes a vibrant community of postdoctoral researchers, graduate students, and
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The Argonne Leadership Computing Facility’s (ALCF) mission is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing
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math, HPC, signal processing, computational physics and materials science. The appointee will benefit from access to world-leading experimental and computational resources at Argonne including some of
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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