79 security-"https:"-"https:"-"https:"-"https:"-"Brunel-University-London" Postdoctoral positions at Argonne in United States
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needs. As a part of this team, you will : Develop, build and test equipment and perform molten salt separations which support the development of a secure fuel supply for MSRs and nuclear fuel cycle
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. Skills in installing, modifying and maintaining equipment. Willingness to abide by safety rules. Ability to work with large volumes of hazardous chemicals. Ability to change projects and work on a variety
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Argonne’s core values of impact, safety, respect, integrity, and teamwork. Preferred Knowledge, Skills, and Experience Experience applying machine learning or AI techniques to scattering, imaging
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design, fabrication, and/or measurement Proficiency in low-noise cryogenic measurement techniques at millikelvin to few-kelvin temperatures Ability to model Argonne's core values of impact, safety, respect
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, and strengthen national energy security. The Postdoctoral Appointee will contribute significantly to ongoing research efforts in resource valorization. Specifically, the candidate will optimize, and
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to develop innovative technologies to improve the efficiency of resource utilization; to minimize our dependence on imported materials; and to enhance our national security. This position is broadly focused
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materials. Skilled oral and writing communication skills with a good record of publication and external recognition in relevant areas. Ability to model Argonne’s Core Values: Impact, Safety, Respect
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, oral and written communication skills, and ability to interact with people at all levels both within and outside the laboratory. Ability to model Argonne’s core values of impact, safety, respect
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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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, safety, respect, integrity and teamwork. Desired skills: Prior research experience in federated learning, distributed learning, or privacy-preserving machine learning. Experience with large-scale model