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, lead large-scale benchmarking across the full stack, and develop scalable classical simulations (e.g., tensor networks)—including performance bounds beyond brute-force classical simulability. This role
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oral communication with a record of leading and reporting results. Desired Qualifications: Knowledge of quantum computing algorithms. Familiarity with tensor network methods. Experience programming GPUs
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resources, and the DOE ESNET network. Develop and apply advanced workflow capabilities that improve performance, portability, and productivity. Perform performance analysis and optimization across end-to-end
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and regional networks of MPAs; ● Collaboratively translating model outputs and valuation approaches into analytic tools to support the planning of individual MPAs in ABNJ; ● Collaboratively identifying
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. These telescopes host a number of new and upcoming instruments designed for high-contrast adaptive optics imaging, high-dispersion spectroscopy, and precision radial velocities. There will also be opportunities
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, under the joint supervision of Prof. Alex Cloninger and Prof. Gal Mishne at UC San Diego. This NSF-funded research focuses on a geometric understanding of training in deep neural networks. The position