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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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clinical use-cases for benchmarking existing state-of-the-art (SOTA) Federated Learning algorithms. This includes running a few pre-processing pipelines. Develop SOTA FL algorithms that tackle data
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on realtime operation and ensuring user privacy across all operations. This thesis will be carried out in tandem with a PhD student in EE working on energy efficiency and sustainability as well as real-time
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opportunities is part of our personnel policy. TUM encourages applications from qualified female candidates. Handicapped applicants will be given preference in the case of equal qualifications. Please note, that
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a part-time position. Interested? Interested candidates please send their documents, including CV and documentation of their academic education to anna.kruspe@tum.de. Technical University of Munich
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) The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance. Data Protection Information: When
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advance. The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance. Data Protection
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are therefore expressly welcomed. The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance
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preference in case of generally equivalent suitability, aptitude and professional performance. Data Protection Information: When you apply for a position with the Technical University of Munich (TUM), you are