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for applications. The ideal 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
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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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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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computing, high-performance computing (HPC), or machine learning (ML) Interest in Standard Model measurements and/or searches for new phenomena Application Review Review of applications will begin once the
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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
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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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characterization, and machine learning. The role offers the opportunity to leverage Argonne’s world-class scientific capabilities and engage with a strong network of internal and industry collaborators. Position
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platform for X-ray absorption spectroscopy by integrating LLMs, scientific machine learning, physics-aware workflows, and strong computational chemistry/electronic-structure expertise. The researcher will
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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