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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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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
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. The team specializes in multi-sensor methods, tensor decompositions and component analysis for the joint processing of multimodal data, notably in the context of invasive (intracardiac electrograms) and non
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nanostructural information from SAXS tensor tomography Investigate how mineralized biological tissues, including bone, adapt their nanostructure to different mechanical and functional demands Help to design and
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computation, probabilistic machine learning, latent-variable models, unsupervised learning, or matrix and tensor factorization is an advantage. Experience with computational methods for large or high
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computation, probabilistic machine learning, latent-variable models, unsupervised learning, or matrix and tensor factorization is an advantage. Experience with computational methods for large or high
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characterization experiments on mineralized biological tissue, in particular small-angle X-ray scattering tensor tomography Publish results in scientific journals and present findings at international conferences
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geometry of asymptotically de Sitter spacetimes to the stress–energy tensor; construct quasi-local and global conformal invariants of spacelike conformal boundaries; use projective compactification to study
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challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and