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addition to research activities, the candidate will be expected to play a pivotal role in mentoring students and fellow post-docs on various material-centric projects, fostering an environment of knowledge sharing and
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experimental cycle from real-time X-ray measurements to post-experiment reconstruction: Develop learning-enabled algorithms for 3D reconstruction of noisy and heterogeneous synchrotron datasets. Implement
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physics), device synthesis, and quantum materials (including optics, photonics, spectroscopy, and optically active point defects). Also, familiarity with nanofabrication (optical and e-beam lithography
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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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Manufacturing group perform science-based membrane synthesis and scaleup development by using roll-to-roll manufacturing and machine learning enabled in-line characterization and quality control methods
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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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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