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Your Job: Chromatography modeling, while crucial for modern bipporcess development, still heavily relies on empirical determination of key model parameters. By combining protein structure
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to domain-specific knowledge and research. All three domains – life & medical sciences, earth sciences, and energy systems/materials – are characterized by the generation of huge heterogeneously structured
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heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange
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explicitly extend these models to capture temporal structure within spike trains thereby moving towards analyses that are sensitive not just to firing rates but also precise timing relationships underpinning
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algorithms Extend the superstructure to tackle AC-PF problems of different complexities and assess its convergence in inference Investigate scaling and performance bottlenecks Explore hybrid ML-classical
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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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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