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descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange isotherm parameters directly from molecular properties. These predictions will be integrated
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. This PhD project focuses on improving how we estimate key parameters in land-surface and ecosystem models, which are essential for understanding climate change impacts. The work involves reviewing existing
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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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network approaches for stress prediction in arterial walls and plaque. Another part of the project is exploring the use of large language models to support neural network design and data preprocessing
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Infrastructure? No Offer Description Work group: IAS-6 - Theoretical Neuroscience Area of research: PHD Thesis Job description: Your Job: This PhD project aims at relating precisely timed spike constellations
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Design and implement clustering and integration approaches (e.g., network-based and subspace clustering) Use co-regulation networks for gene function and protein–protein functional relationship prediction
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Your Job: This PhD project develops a Bayesian inference framework for hybrid model- and data-driven modeling of metabolism, with a particular focus on handling model misspecification. By combining
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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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Infrastructure? No Offer Description Work group: IAS-6 - Theoretical Neuroscience Area of research: PHD Thesis Job description: Your Job: This PhD project bridges between classical analytical methods and modern AI
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for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics-aware learning methods with domain decomposition techniques, enabling parallel training and