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: Quantum information and quantum computation Quantum many-body physics and quantum simulation Tensor networks and classical simulation algorithms Machine learning for quantum computing Scientific software
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on developing, implementing, and applying tensor-network and related quantum-dynamics methods. Applicants may indicate a preference for one project or ask to be considered for both. The start date is flexible
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. Experience in the development of tensor network methods and/or quantum algorithms will be considered an asset. Website for additional job details https://www.dfa.unipd.it/servizi/amministrazione-trasparente
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[Srdinsek2025], the sole structure of the tensor networks that could be “salvaged” was the canonical form that provided the ability to dynamically compress/decompress. During this PhD, we will develop more; we
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, high-performance scientific computing, tensor methods, and scalable algorithms. Optimization: continuous and discrete optimization, computational optimization, inverse problems, and optimization methods
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. Numerical analysis and scientific computing: numerical methods, computational linear algebra, high-performance scientific computing, tensor methods, and scalable algorithms. Optimization: continuous and
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to start imminently, and before mid October this year. The project intended for the post-doc is part of a collaboration within my group on the Google Willow Chip project (https://www.bbc.co.uk/news/articles
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top choices of courses for the Winter 2027 semester by uploading the completed TA application form to your application in Workday: https://www.mcgill.ca/hr/files/hr/1_
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, for example, picosecond pump-probe experiments, 3D SAXS tensor tomography, X-ray photon correlation spectroscopy, pytchography and coherent SAXS imaging. Formally, this position is classified as Asisstant with
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further appears in the approximation of various dynamical problems by tensor networks and neural networks. In all these cases, the parametrization is typically irregular, meaning that the occurring linear