49 machine-learning "https:" "https:" "https:" "Universiteit van Amsterdam" Postdoctoral positions at Argonne
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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 High Energy Physics Division at Argonne National Laboratory invites applications for a postdoctoral research associate position to conduct research in machine learning (ML) for applications in
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development, and publication in peer-reviewed venues. Strong background in machine learning, with research experience in deep learning, foundation models, or related areas. Solid programming ability in Python
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The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing
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The Advanced Photon Source (APS) (https://www.aps.anl.gov/ ) at Argonne National Laboratory (Lemont, Illinois, US (near Chicago)) invites applicants for a postdoctoral position to develop and
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an integrated framework to explore advanced workloads including simulations with in-situ visualization and, possibly, machine learning integration. This work will inform future ALCF platform procurement decisions
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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clustering, redshift-space distortions, weak/strong gravitational lensing, and artificial intelligence/machine learning (AI/ML). The observational focus is on optical sky surveys (DES, DESI, Roman, Rubin Obs
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partners, and other national laboratories. Objective: Enhance THAPI: Extend and optimize the THAPI profiler (https://github.com/argonne-lcf/THAPI ) to concurrently profile AI/ML and ModSim components
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the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis