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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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within the Faculty. Along with the disciplinary approach a very ambitious interdisciplinary research culture has been developed. The faculty's research and teaching focuses on social, economic, political
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] and taphonomy of animal bones [Cifuentes-Alcobendas and Dom´ınguez-Rodrigo, 2019] are gradually intensifying. Thus, the present PhD project is an opportunity for the development of original ML solutions
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, but are not limited to, neural coding in the visual cortex, multimodal information processing, state-dependent processing, visual perception, development of imaging tools for in vivo neuronal recordings
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only to healthy knee bone structures in the context of elective orthopedic surgery [5, 6]. Moreover, many of these models are developed by private companies for implant placement, limiting accessibility
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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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Wikibase instance Curate and model historical migration datasets within the dedicated Wikibase instance Contribute to the design of ontologies and metadata schemas for the knowledge graph Develop data-driven
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within the Faculty. Along with the disciplinary approach a very ambitious interdisciplinary research culture has been developed. The faculty's research and teaching focuses on social, economic, political
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train robust machine learning (ML) algorithms without exchanging the actual data. The benefits of such a decentralized technology over personal and confidential data are multiple and already include some
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modeling the dynamic of the data evolution is clearly important. The purpose of this postdoc position, within the Institut 3IA Côte d'Azur (Univ. Côte d’Azur & INRIA), will be focused on the development and