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-scale nature, complexity, and heterogeneity of 6G networks, we use tools such as artificial intelligence/machine learning, quantum computing, graph theory, graph-signal processing, and convex/non-convex
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-learning models (e.g., graph neural networks, equivariant architectures) in collaboration with computer science researchers. Applying developed models to problems in Earth and planetary interiors, such as
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systems, numerical analysis, stochastic problems and stochastic analysis, graph theory and applications, mathematical biology, financial mathematics and mathematical approaches to signal analysis
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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, the QCILab
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with Prof. Mauro Maggioni on problems that include: analysis and algorithms on graphs, geometric analysis of high dimensional data sets, dynamic data sets, scientific machine learning, high-dimensional
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relevant to modern data science (e.g., Bayesian or frequentist inference, information theory, uncertainty quantification, high-dimensional methods). Programming skills in Python and/or R, with evidence of
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architecture and design for complex socio-technical systems Graph theory, network science, and knowledge representation Agent-based and simulation modeling AI/ML, foundation models, causal inference, and
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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
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computer science using data-driven techniques (graph theory, ICA, machine learning), in other imaging modalities (DTI; MEG), and in multimodal integration will be relevant. Experience with AFNI/SUMA, SPM, FSL