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, this PhD thesis will focus on the characterization of multipolar electrograms (EGM) through graph signal processing (GSP). The underlying hypothesis is that local propagation patterns in AF are associated
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aerosols. - Visualization and post-processing tools: Proficiency in visualization tools (e.g., Python) and data post-processing tools to analyze simulation results and generate graphs, maps, and diagnostics
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programming, microfluidics, and engineering to design sophisticated algorithms for exploring and manipulating information encoded in DNA-based graphs. The position is fully funded by a prestigious CNRS
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to have a sound basis in discrete math, graphs, propositional logic, and algorithms. Some background in complexity theory would be an advantage. Due to the international supervising team, the working
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, particularly Graph Neural Networks (GNNs), which show great promise. These methods have already demonstrated performance at least comparable to current Track Finding algorithms, with significant room for further
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representation language. The thesis will particularly investigate approaches based on attributed graphs to model infrastructures, their topological relationships, inspection-derived observations, and the levels
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al., “Deep Transfer Learning for Fault Diagnosis”, IEEE Transactions on Industrial Electronics, 2020. • Zhang C. et al., “Graph Neural Networks for Power Systems”, Electric Power Systems Research, 2023