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computational materials science aligned with CNM strategic themes and the DOE mission Publish in refereed journals and present at conferences, symposia, and seminars Contribute to proposal development and assist
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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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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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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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We invite you to apply for a Postdoctoral Appointment with Argonne’s Electrochemical Materials and Interfaces Group in the Materials Science Division. The purpose of this appointment is to perform
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for critical energy and technology sectors. Ability to assess the economic and operational impacts of large-scale AI adoption (e.g., data centers, compute infrastructure) on U.S. electricity demand, generation
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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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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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. 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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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