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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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: 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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, 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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[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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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
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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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on computational many-body techniques, particularly Density Matrix Renormalization Group, Tensor Network, Exact Diagonalization and Monte Carlo simulations are strongly encouraged to apply. To apply, candidates
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from multi-omics datasets (epigenomics, transcription, sequence). • Using representation learning (tensor factorization framework e.g., AVOCADO) extend the predictive framework allowing imputation in
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: exact diagonalization, Monte Carlo, and where appropriate, tensor network techniques. The postholder should also have a broad familiarity with the theory of strongly correlated systems and excellent oral
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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