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? Set up a network model to reproduce the main results and provide potential neuronal mechanisms. Existing recordings with optogenetic inactivation could be leveraged to causally verify or reject important
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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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or spike correlation patterns limited to local neural circuits or span across brain regions? Set up a network model to reproduce the main results and provide potential neuronal mechanisms. Existing
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, or machine-learning frameworks is an asset Strong analytical skills with a solid understanding of data evaluation, modeling, and interpretation of complex datasets Ability to work independently as
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, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular geometries. Current simulation-based approaches require complex 3D meshes and are often too slow
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in complex digital projects - Conocimientos en testing de usabilidad y análisis de métricas UX mediante herramientas especializadas (Maze, Useberry) // Knowledge of usability testing and UX metrics
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the Faculty of Applied Science as well as medical trainees. Work Performed Research, develop, implement, and evaluate complex projects. Present findings and recommendations to the research team. Oversee and