20 parallel-computing-numerical-methods-"Prof" Postdoctoral positions at University of California
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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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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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An exciting postdoctoral position is available in the exciting field of mathematics of deep learning, under the joint supervision of Prof. Alex Cloninger and Prof. Gal Mishne at UC San Diego. This NSF-funded
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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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, 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
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on superconducting QPUs. Knowledge of noise and error sources in superconducting systems. Familiarity with benchmarking and characterization methods for quantum computers. Experience with tensor network methods and
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Davis comprises four faculty (Profs. Chertok, Citron, Conway, Erbacher), one senior researcher, and a number of postdoctoral researchers and graduate students. The successful candidate will work under the
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• Demonstrated experience in computational or quantitative research methods. • Strong programming skills in Python. • Experience with high-performance computing, geospatial data, and causal inference methods
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if the position has not yet been filled. Position description The Physics Department at the University of California, Santa Barbara invites applications for 1-2 postdoctoral research positions in Prof. Brendan
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and diverse astronomy group. The successful applicant(s) will work in collaboration with Prof. Caitlin Casey on a variety of observational datasets and projects spanning JWST (especially focused