80 computer-science-programming-languages-"UCL"-"UCL" Postdoctoral positions at Argonne in United States
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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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We invite you to apply for a Postdoctoral Appointee position in the Chemical Sciences and Engineering Division (CSE) at Argonne National Laboratory. This position offers the opportunity
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information science principles Proficiency in microwave and/or optical device characterization Desirable Skills: Expertise in quantum experiments, such as qubit control and entanglement Experience with quantum
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The Chemical Sciences and Engineering Division is seeking applicants for a postdoctoral appointee who will conduct computational research in Selective Interface Reactions (e.g., atomic layer
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nanofabrication. Experience in microwave and optical device characterization and measurement. Knowledge and good understanding of quantum information science. Experience with superconducting qubit design
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
The Mathematics and Computer Science (MCS) Division at Argonne National Laboratory invites outstanding candidates to apply for a postdoctoral position in the area of uncertainty quantification and
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of deposition science and heterogenous interfaces. Position Requirements: A PhD in chemistry, materials science or related field; received within the last 5 years or upcoming year. Significant written and oral communication
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and program managers. Position Requirements Minimum Education / Experience Requirements: A Ph.D. in physics, applied physics, electrical engineering, or related field. Additional Requirements: Normal
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physics, etc. Proficiency in Python or other scientific programming languages. Programming skills in numerical methods for image processing and AI/ML methods for quality improvement are advantageous
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techniques to solve pressing challenges in energy storage. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne