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algorithms for dynamic structured data, with a particular focus on time sequences of graphs, graph signals, and time sequences on groups and manifolds. Special emphasis will be placed on non-parametric
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revisit discretization methodologies in view of modern requirements and computational capabilities. The candidate will focus on developing mesh generation algorithms meeting the following criteria
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. The monitoring of telecommunications and energy production and distribution networks are characteristic examples of such time-critical applications. The project aims to propose unsupervised online CPD algorithms
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, sensor failures, or the aggregation of datasets from multiple sources. There is a rich literature on how to impute missing values, for example, considering the EM algorithm [Dempster et al., 1977], low
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motivated the development of Federated Learning (FL) [1,2], a framework for on-device collaborative training of machine learning models. FL algorithms like FedAvg [3] allow clients to train a common global
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training of an artificial intelligence algorithm capable of automatically segmenting the bony structures of both healthy and fractured tibial plateaus. This will serve three main purposes: 1) Enable
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. Requirements: We are looking for a highly motivated candidate with a PhD in theoretical/computational physics or applied mathematics. A strong background in quantum mechanics and numerical simulations is
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algorithms will be developed to extract discriminative and predictive features from a multimodal dataset consisting of digital histopathological images, lung CT images, clinical, genomics, and multiproteomics
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techniques have been demonstrated to enable spin-wave imaging with spatial resolution down to 50 nm with excellent signal to noise ratio. The PhD/postdoc will focus on investigating unexplored spin-wave
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molecular targets critical for developing new therapies for rare diseases, based on genetic data and biological system simulations. -Computational Drug Repurposing: Developing novel algorithms and databases