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Physarum polycephalum is renowned for solving complex tasks despite its simple make-up consisting of only one giant cell that harbors millions of cell nuclei. You will investigate the role of nuclei as
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. You must hold a master’s degree in Electrical Engineering, Computer Science, Mathematics, or similar. The work is interdisciplinary and we will closely collaborate with a group at the chemistry
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machine learning technologies. This PhD position is part of the project “Artificial Intelligence for the automated creation of multi-scale digital twins of the built world”, which is funded via the Georg
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. The position is hosted at the Chair for Algorithms and Complexity, headed by Prof. Susanne Albers (http://wwwalbers.in.tum.de/index.html.en). The dissertation work will involve research in the fields
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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