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learning to push our understanding of the robustness and explainability of Federated Learning models. Your responsibilities: Build and create clinical use-cases for benchmarking existing state-of-the-art
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learning to push our understanding of the influence of data heterogeneity including non-iid and domain shift on Semi-/Fully-supervised Federated Learning algorithms. Your responsibilities: Build and create
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on a part-time (65%) basis for an initial period of 1 year, with the option to prolong for another three years. Research project and work environment You will have the great opportunity to shape a
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