13 parallel-processing-"International-PhD-Programme-(IPP)-Mainz" Postdoctoral positions at University of California
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. Experience implementing, optimizing, or integrating quantum libraries such as Itensor, CUDA-Q, Qiskit, or PennyLane. Experience debugging and profiling distributed-memory parallel applications. Knowledge
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Mathematics, or a related field, awarded within the last five years Programming experience in one or more of Python, C++, Fortran, or Julia Knowledge of high-performance and parallel computing Experience
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letters (at least one addressing teaching) Please reference job #09900 in the subject line of all correspondence. Applications are welcome at any time. The review process starts November 1, 2025 and will
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Will Do: Lead large-scale benchmarking of full-stack quantum processing units (QPUs). Develop and validate performance bounds for QPUs beyond brute-force classical simulability. Design and conduct
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in building and testing quantum computer control systems. Assist in developing and testing PCB boards. What is Required: Ph.D. degree in physics, applied physics, electrical engineering, or a related
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of application) Applicants must have completed all requirements for a PhD Degree in geography, urban planning, sociology, political science, economics, data science, public policy, computer/information
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to influence policy and technology development. Experience with food-energy-water analysis. Experience with technoeconomic analysis/process simulation Experience mentoring students Must be able to start no later
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University of California, Berkeley, Department of Electrical Engineering and Computer Sciences Position ID: University of California, Berkeley -Department of Electrical Engineering and Computer
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University of California, Berkeley, Department of Electrical Engineering and Computer Sciences Position ID: University of California, Berkeley -Department of Electrical Engineering and Computer
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programming skills are essential, along with a track record of published papers and strong self-motivation. - Prior experience in the areas of machine learning / data-driven methods, signal processing and/or