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optical transition and favorable spin properties of individual solid-date erbium ions (Er3+) to store quantum information necessary for practical, robust, and scalable quantum communication. The focus
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Because of the drastically increasing demand from AI/ML applications, the computing hardware industry has gravitated towards data formats narrower than the IEEE double format that most computational
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scalability studies to identify and improve bottlenecks in large codes. Experience in development of data-driven reduced-order models in one or more of these areas: turbulence, boundary layer flows, combustion
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optical and THz techniques. Ability to analyze and understand complex data set is required. Experience to lead ultrafast x-ray scattering or electron scattering experiments is a plus but not required
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optimization schemes. From developing AI models to uncover structure-function relationships with limited data sets, to building automated electrode-electrolyte interface discovery workflows and implementing full
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techniques to solve pressing challenges in energy storage. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne
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science for quantum information hardware with the industrially mature solid state platforms of silicon/silicon germanium and silicon carbide spin qubits. The position will focus on heterogeneous integration
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, MATLAB, or similar programming environments for instrument control and data analysis. Excellent written and oral communication skills. Demonstrated ability to work both independently and collaboratively in
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computational scientists to advance a next-generation, user-friendly, agentic AI platform for automated data analysis, interpretation, and user interactions. The appointment is expected to last two years and the
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fabricate nanoscale electrical test structures (e.g., photolithography, e-beam lithography) Design, test, and characterize radiofrequency (RF) circuitry and measurement approaches Analyze and interpret data