24 learning "https:" "https:" "https:" "https:" "https:" "DAAD" Postdoctoral positions at Argonne
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Publications: 1. P. Chen et al., Ultrafast photonic micro-systems to manipulate hard X-rays at 300 picoseconds, Nat Commun, 10:1158 (2019). https://doi.org/10.1038/s41467-019-09077-1. 2. P. Chen et al., Optics
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simulations on the Aurora supercomputer, using AMReX (https://amrex-codes.github.io/amrex/ ) and the lattice Boltzmann method (LBM). The candidate will develop flow/geometry-aware refinement strategies that go
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Qualifications: Experience using the MOOSE simulation framework is highly desired. Experience fitting complicated physics-based models against test data, including machine learning and Bayesian optimization
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, focused ion beam specimen preparation, and computer vision or machine-learning analysis of microscopy datasets. The position requires strong experimental, analytical, written, oral, and interpersonal
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The Argonne team is seeking two highly motivated postdoctoral researchers to help shape the next generation of secure, scalable, and continuously learning AI systems for biomedical discovery
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ideal for someone who enjoys working at the intersection of data science, machine learning, materials research, and experiment, and who is motivated to translate computational advances into real
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learning (ML) to address future physics and detector challenges. Current physics interests include Standard Model measurements and searches for new phenomena. We welcome applicants who are excited
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Postdoctoral Researcher in artificial intelligence and machine learning (AI/ML) for advanced tuning and diagnosis of particle accelerators. The Accelerator Operations and Physics (AOP) Group
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) at 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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learning, or optimization Strong programming skills in Python and experience with scientific computing and machine-learning libraries Ability to work across experimental, robotic, and computational systems