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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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lead efforts to develop experimental techniques using conventional and coherent imaging in the ultrafast time domain, as well as a computational framework for modeling and reconstructing images
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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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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
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scientists with extensive microelectronics (materials and devices), AI, computational materials science and materials characterization expertise; and will be expected to bring the electrochemical expertise
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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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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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. Preferred Knowledge, Skills, and Experience Prior experience with high-throughput or computational protein design/screening techniques. Background in structural biology (CryoEM/crystallography) Knowledge
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The X-ray Imaging Group (IMG) of the Advanced Photon Source (APS) is seeking a postdoctoral researcher with expertise in computational science and image processing to develop innovative methods
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focus on further advancing the ATTA technique. The Physics Division has an active and broad-ranging program at the intersection of nuclear and atomic physics including a strong focus on fundamental