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avenues to increase the function of relevant DCs subsets to increase the formation of tissue, resident, long lasting memory T cell responses. This project will be developed in the context of an influenza
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of teaching and research, the FSTM seeks to generate and disseminate knowledge and train new generations of responsible citizens in order to better understand, explain and advance society and environment we
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In cancer as well as chronic infections, T cells are exposed to persistent antigens and acquire a dysfunctional gene expression program which includes high expression of the inhibitory receptor
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of persistent AF. However, this therapy depends heavily on the practitioner’s subjectivity, with rather variable protocols and success rates reported by different centers. The development of robust, widely
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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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of teaching and research, the FSTM seeks to generate and disseminate knowledge and train new generations of responsible citizens in order to better understand, explain and advance society and environment we
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addition, this role also offers the opportunity to develop independent research projects aligned with the group’s overarching goals. You are expected to learn and implement innovative techniques, participate
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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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and interpretation. Prominent examples include time sequences on groups and manifolds, time sequences of graphs, and graph signals. The objectives The project aims to develop unsupervised online CPD
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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