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, technical reports, software tools, and project deliverables. Present research results at technical meetings, workshops, and leading conferences, and publish in high-impact journals. Position Requirements
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We are seeking a highly motivated postdoctoral researcher to conduct independent research on foundation models for scientific and engineering applications, with an emphasis on training, adaptation
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applying machine learning or other elements of artificial intelligence to solving significant scientific or engineering problems Interest in software development, with particular emphasis on the Python
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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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physics (HEP) and nuclear physics (NP) experiments. The successful candidate will be a key member of a multidisciplinary co-design team integrating materials science, computing, and device engineering to
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The AMO Physics group within Argonne’s Chemical Sciences and Engineering Division (CSE) invites applications for a Postdoctoral Appointee position. Our research investigates fundamental x-ray and
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collaborating with a software engineering team to translate research into production-ready tools. The successful candidate will be part of an inter-lab, highly inter-disciplinary team of experts in ML, applied
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, LDAV, eScience, ISC, SC) Contribute software, prototypes, and design patterns to shared research infrastructure at ALCF and across DOE projects This postdoctoral position directly supports ALCF’s mission
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The Data Science and Learning Division (DSL) of the Computing, Environment and Life Sciences Directorate (CELS) and the Materials Science Division (MSD) of the Physical Sciences and Engineering
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databases Collaborate closely with experimental teams to inform model development and interpret results Contribute to software development, documentation, and reproducible workflows Disseminate findings