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infrastructure, and domain science. The candidate will have established expertise across automated and autonomous experimental platforms and AI in addition to leadership of multi-disciplinary research programs and
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infrastructure, and domain science. The candidate will have established expertise across automated and autonomous experimental platforms and AI in addition to leadership of multi-disciplinary research programs and
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catalyst design. This role involves conducting multiscale modeling, spectroscopy simulations, and the development of machine learning methods and automated workflows for multi-fidelity, multiscale, and
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systems Advanced cleanroom fabrication facilities, including the Center for Nanoscale Materials (CNM) Why Join Argonne This is an opportunity to contribute to cutting-edge research at the intersection
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Core Values of Impact, Safety, Respect, Integrity, and Teamwork. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type Full time The expected hiring
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Argonne’s core values of impact, safety, respect, integrity, and teamwork Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type Full time The expected hiring
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communication skills Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type
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communication skills Record of publications, presentations, or other technical outputs commensurate with career stage Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork
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within Argonne and with universities, national laboratories, government agencies, and other research organizations. Core Responsibilities: Conduct independent and collaborative research in quantum
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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory invites applications for a postdoctoral research position focused on developing AI/ML methods for autonomous materials