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at Materials Engineering Research Facility (MERF) and collaborators inside and outside Argonne. The candidate is expected to design and conduct experiments, analyze data and explore mechanisms behind
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optimization, with experience in adaptive routing and SDN technologies. Proficiency in programming languages such as Python, C/C++, and experience with parallel computing frameworks. Effective written and oral
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, XRD, XPS, and FTIR to study membrane structure and composition Analyze experimental data, maintain thorough documentation of research activities and results Disseminate findings through scientific
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should have a strong background in experimental physics and/or engineering, or in a relevant field. Experience in superconducting qubits, nanofabrication, photonics, and low-temperature experiments
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statistics and uncertainty quantification (Bayesian analysis, data assimilation, Gaussian process modeling), and their application to complex systems modeling. Experience in developing and applying statistical
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algorithms with mainstream programming languages such as Julia, Python, Java, C/C++, etc. Proficiency in writing scientific research articles and presenting results at academic conferences. Experience with
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related field at the PhD degree level with zero to three years of experience or equivalent in the scientific application of this knowledge and practical laboratory experience. To perform the essential
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-aware multi-modal deep learning (DL) methods. At Argonne, we are developing physics-aware DL models for scientific data analysis, autonomous experiments and instrument tuning. By incorporating prior
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, project presentations, and other regular channels. Position Requirements Skill in modeling, processing, and analyzing computational results to inform accompanying experimental efforts. Skill in the use
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experimental data. Experience in GPU programming. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type Full time The expected hiring range for this position is