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geometries. Current simulation-based approaches require complex 3D meshes and are often too slow for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics
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population-level neural interactions. Prior work has emphasized rate-based codes due to their relative simplicity; our approach will explicitly extend these models to capture temporal structure within spike
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complexities and assess its convergence in inference Investigate scaling and performance bottlenecks Explore hybrid ML-classical approaches, the application of meta learning, and the integration of convex
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progress on the most complex known systems Outstanding scientific and technical infrastructure A highly motivated group as well as an international and interdisciplinary working environment at one
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no. 1 scholarship grant for scientific training activities at INFN Structure of Bari for the following research topic: “Topological defects in the dynamics of field models for complex fluids”. Where