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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
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postdocs and three PhD students with complementing competences. In this call, we are looking for one postdoc who is mathematically oriented, with strong background in systems theory and control, and one
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following: Data Science, Machine Learning, Computational Social Science, Big Data. Relevant skills could include statistical analysis, data management and collection, causal inference, network analysis, graph
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differential equations, computational fluid dynamics and material science, dynamical systems, numerical analysis, stochastic problems and stochastic analysis, graph theory and applications, mathematical biology
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Quantum computing and Graph theory In this role, you will be responsible for conducting research on graph theoretic approaches to design quantum photonic experiments. Additionally, the position involves
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Physics, Spectral Theory, Quantum Chaos, Large Graphs and Quantum Walks. Related areas such as Quantum Information can also be considered. This position is offered through the research funds of Mostafa
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project, we are looking for a strong candidate to contribute to the development of quantum algorithms and applications, focusing on quantum walks and quantum machine learning on graph structures. Your