63 computer-programmer-"https:"-"Inserm"-"UCL"-"FEMTO-ST"-"https:" positions at Argonne
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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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. This position offers an exciting opportunity to contribute to fundamental and applied research in materials chemistry using advanced computational techniques and artificial intelligence. The project involves: 1
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community In addition, the Group Leader is expected to develop and lead a world-class research program that strongly aligns with DOE priorities in low-energy nuclear physics, as articulated in the 2023
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Extensive knowledge of Microsoft Excel and good computer programming skills Knowledge of techno-economic analysis and life cycle analysis Experience working with Argonne’s EverBatt model, GREET model, and
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to the development of new research directions aligned with program goals. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in Chemical Engineering, Materials
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detectors while also having flexibility to pursue your own research interests. Research Focus Participate in a detector R&D program aimed at developing superconducting nanowire sensors to enable
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limited to, ATLAS at CERN, the South Pole Telescope, and the Simons Observatory. The candidate is also expected to work closely with computational experts at the Computational Science (CPS) division
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looking for candidates whose research program aligns with the 2023 Long Range Plan for Nuclear Physics, focusing on lab-based tests of fundamental symmetries via precision experiments. The ideal candidate
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experiment research program, particularly in the ATLAS experiment. The successful candidate is expected to take a leading role in data analysis, detector construction, and experiment operations. In
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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