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complexity and large number of parameters. Tensors are a natural way to represent high dimensional data for numerous applications in computational science and data science [1]. CP, Tucker and Tensor Train are
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complexity in practice might provide fewer benefits as it could be expected from analytical derivations. The reason is the overhead caused by specific hardware we use to train or execute neural networks
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industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc. Contexte et atouts du poste Context and background
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investigate several facets of the algorithms and the design processes, and ultimately provide this application to the broad scientific community. The main goal of the PhD project is to develop a deep generative
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, nematode, humans). Finetune the model parameters based on the above mentioned biologically data and derive a neurophysiologically plausible interpretation. +Develop a novel training framework that takes
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industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc. Contexte et atouts du poste The thesis will take place in
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analysis in dynamic contact networks through AI-driven methodologies, focusing on two distinct use cases: urban bike-to-infrastructure networks and LEO satellite systems. The aim is to develop and validate