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- Autonomous University of Madrid (Universidad Autónoma de Madrid)
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Partial Differential Equations, Dynamical Systems, Optimization, Scientific Computing, Complex Systems, and Network Science. Spanish language skills are not required. Our working language is English. Both
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physics, network science, multiscale systems, and modeling of complex phenomena. Computational modeling and simulation: reduced-order modeling, simulation, data assimilation, digital twins, and
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than before. Coordinated by Prof. Dr. Heli Harvala, Professor of Virology at the University of Turku in Finland, the network brings together 8 academic institutions and 14 associated partners from across
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. Heli Harvala, Professor of Virology at the University of Turku in Finland, the network brings together 8 academic institutions and 14 associated partners from across Europe. Within DETECTIVE, researchers
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temporal resolution. Background in network science, statistical physics and complex systems. (Optional) Familiarity with information-theoretic measures (Transfer Entropy / Symbolic Transfer Entropy) and the
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capillary pressure and hysteresis in multiphase flow, and the complex phase behavior of multicomponent mixtures described by realistic equations of state. Achieving this goal requires an integrated approach
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computing and reproducible research. (Optional) Experience in time-series analysis, with explicit attention to intraday temporal resolution. Background in network science, statistical physics and complex
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researchers of any nationality interested in apply for postdoctoral fellowships in the Australia-Spain Network for Innovation and Research Excellence (AuSpire) is a postdoctoral training program offering
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“EXODOCS” Research Project to which it is assigned: MSCA-DN EXODOCS – ExoMars Rosalind Franklin Doctoral Network Funding Entity: European Commission, within the framework of the Marie Skłodowska-Curie
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for cell transplantation therapies in animal models of Alzheimer's disease, stroke, and epilepsy. To achieve this goal, the candidate will combine a gene network-based approach with a machine learning model