14 learning-"https:"-"https:"-"https:"-"https:"-"NIST" Postdoctoral positions at Argonne
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availability. Relevant 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
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solver, capable of running 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
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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. This
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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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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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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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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 of the
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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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machine 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
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, including machine learning and Bayesian optimization approaches. Specific experience modeling the performance of advanced engineering ceramics and/or the high temperature performance of metal alloys