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on the development and analysis of continuous and discrete models in connection with convex and nonconvex optimization problems and monotone inclusion systems. Our ideal candidate already has experience with modern
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algorithms for entanglement distribution in quantum networks. The purpose of the role is to contribute to the project “Utility Optimization in Quantum Networks: Algorithm Design and Analysis”, working with Dr
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on the design and performance analysis of resource allocation algorithms for entanglement distribution in quantum networks. The purpose of the role is to contribute to the project "Utility Optimization
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an excellent publication record. Solid research experience in one or more of the following topics is expected: Graph neural networks Optimization algorithms Predicting structured output Self-supervised learning
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, mathematical finance, or optimization and is capable of actively contributing to research projects in these fields. The contract start date is flexible, beginning as early as March 1, 2026. The employment
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optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative AI, or active learning for materials applications. Integration of theory and experiment: Using computation and