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-efficient computing Developing mathematical modeling for complex scientific problems Designing algorithms to improve the performance of scientific applications Researching digital and post-digital computer
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questions in quantum information science, and to guide near-term hardware and algorithm co-design. What You Will Do: Design of superconducting electronic devices with emphasis on superconducting quantum
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of deep learning-based denoising frameworks and sub-tomogram alignment for cryo-electron tomography datasets, to support the analysis of the performance of various denoising algorithms. This role will
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the total compensation value with benefits. Qualifications Experience developing software to take practical advantage of state-of-the-art ML algorithms and research results in AI. (Required) Experience
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(Required) Experience creating and utilizing statistical algorithms and conducting power calculations to determine sample size to answer complex research questions. (Required) Demonstrated experience working
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data and analyzes outcomes. Performs research on current and future HPC, data, and CI technologies, hardware and software. Works on algorithm development, optimization, programming, computer science
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. Collaborate with JGI scientists and researchers across Berkeley Lab, including the Computational Research Division, to evaluate and implement new methods and algorithms that enhance system capabilities and
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needed to resolve foundational questions in quantum information science, and to guide near-term hardware and algorithm co-design. What You Will Do: Characterization of noise and errors (including non
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systems. Provide guidance and recommended techniques for warehousing and accessing critical information. Recommend and implement tools and algorithms to generate reports and visualization dashboards using
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questions in quantum information science, and to guide near-term hardware and algorithm co-design. What You Will Do: Design of superconducting electronic devices with emphasis on superconducting quantum