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formal results in distributed control theory and graph theory applied to collective behavior. - Development of techniques based on Lyapunov functions, robustness analysis, and resilience to imperfections
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processing: EEG. Brain connectivity. Graph theory. Professional Experience: Participation in multidisciplinary teams with doctors and engineers. Have carried out experiments with TMS and recording of EEG
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build using molecular dynamics, the MACE foundation models and density functional theory. Main Tasks and responsibilities: AI4LSQUANT aims to accelerate quantum modelling by learning fast, accurate
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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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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