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math, HPC, signal processing, computational physics and materials science. The appointee will benefit from access to world-leading experimental and computational resources at Argonne including some of
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chemistry, chemical engineering, physics, computational science, materials science, or related field. Background in synchrotron characterization techniques. Experience collecting and analyzing large
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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory seeks a highly motivated postdoctoral researcher to join a multidisciplinary team advancing quantum information
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The Surface Scattering and Microdiffraction (SSM) group in the X-ray Science Division (XSD) at the Advanced Photon Source (APS), Argonne National Laboratory is seeking Two Postdoctoral Appointees, both focused on multimodal synchrotron characterization of defects and interfaces in oxides and 2D...
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-throughput workflows for data acquisition and analysis Contribute to on-the-fly data processing and integration with computational tools Collaborate with multidisciplinary teams in nanofabrication
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, distributions, and dynamics in metallic, oxide, and semiconducting systems. This project integrates high-throughput and in situ TEM experimentation with AI/ML-driven image analysis and computational modeling
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information science and light–matter engineering, while engaging with CNM’s cleanroom and characterization capabilities, APS ultrafast and nanoprobe X-ray beamlines, MSD’s THz initiatives, and Q-NEXT’s national quantum
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techniques in interfacial science; and mathematical techniques and computer programming for data analysis. Considerable skill in working interactively and productively in a multidisciplinary environment Good
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engineering principles Experience working safely with hazardous materials using engineering controls such as gloveboxes is desired. Knowledge of the use of computers to design and control experiments and to
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experience in economic and supply chain analysis, computational modeling, or policy analysis. Proficiency in scientific programming languages (e.g., Python, R) and data analysis libraries (e.g., pandas, NumPy