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on the development of innovative tensor network methods to support the design, analysis, and optimization of quantum and quantum-inspired algorithms. Theoretical and computational tools will be developed
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About the Role To work with Dr Scott Balchin on the EPSRC funded project “Point-free methods in tensor-triangular geometry.” This EPSRC funded research project aims to use tools coming from point
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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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, 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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[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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the simulation of turbulent flows using a tensor network representation of the Navier–Stokes equations. Unlike recent approaches based on tensor networks, which simulate fluid flows in physical space using finite
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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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mathematics, and knowledge in deep learning. Practical experience in a broad range of techniques including LLM training, evaluation, RLVR, PEFT, quantisation, tensor/data parallelism. Ideally, familiarity with