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beyond traditional error estimators, creating physics-based adaptation algorithms that intelligently predict where refinement will be most beneficial for smarter, more efficient simulations. We seek
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evaluate advanced algorithms for applications such as secure and adaptive control, anomaly and attack detection, resilient decision-making, and AI-enabled operational support for highly distributed grids
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in materials for electrochemistry. While the focus in on computational expertise, this position will involve some experimental work in adapting workflows for automation and artificial intelligence
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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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-reviewed journals. Ability to work independently on a day-to-day basis. Willingness to work a non-standard schedule to accommodate testing needs, including occasional afternoons and weekends. Skilled in
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, which specialize for a restricted set of floating point operations only. Many scientific applications, particularly those that are physics-driven and mission-critical, still struggle to adapt to this new
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(SNSPDs) Superconducting electronics and sensors Device/Detector simulations Application Instructions Cover Letter Curriculum Vitae (CV) Statement of Research Interests Additionally, arrange for three
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. Conduct large-scale LLM training, including pretraining, fine-tuning, RL tuning, and domain-specific adaptation on HPC systems. Design and implement fine-tuning and RL strategies to optimize LLM alignment
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school transcript CV (including publications and presentations) Additionally, arrange for three letters of recommendation to be sent to HEPHR@anl.gov . This position will remain open until filled. Position
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research interests to Ian Cloët (icloet@anl.gov ). 3. Arrange for three letters of recommendation to be emailed to the same address. Any questions about the position may be sent to Ian Cloët (icloet@anl.gov