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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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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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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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EM-General-ZSFG Full Time 87585BR Job Summary This position is well suited for recent university graduates seeking structured, full-time research experience prior to pursuing advanced training (e.g
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environment at the interface between neuroscience and digital technologies, enabling scientific progress on the most complex known systems Outstanding scientific and technical infrastructure A highly motivated
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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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-products. Current extracts from whole rice, rice bran, and rice hulls will be evaluated for biological activity, followed by purification and structural characterization of active components. This research
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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