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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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of electronic Hamiltonians. The postdoctoral researcher will develop graph neural networks based on the MACE architecture to predict Hamiltonian elements for 2D materials and van der Waals heterostructures, with
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following areas: Probabilistic or Bayesian Machine Learning Variational Inference, Ensemble, or Diffusion Models Spatio-Temporal or Sequential Modelling Graph Neural Networks Deep Learning and Uncertainty
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associate to work on one or more of the following topics: Mathematical Physics, Spectral Theory, Quantum Chaos, Large Graphs and Quantum Walks. Related areas such as Quantum Information can also be considered
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has strong research groups in analysis, combinatorics, computational mathematics, differential equations, dynamical systems, geometry, graph theory, mathematical biology, mathematics education, number
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, graph theory, graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and supported by the COMMLab , the 6GSPACE Lab , the HybridNetLab
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to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal
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include: Strong foundation in one of the following areas: Machine Learning / Information Retrieval / Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date
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, computational fluid dynamics and material science, dynamical systems, numerical analysis, stochastic problems and stochastic analysis, graph theory and applications, mathematical biology, financial mathematics
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the theory of quantum graph states. Additional expertise in computational methods would be useful but is not necessary. The Postdoctoral and Senior Research Associate positions will also involve