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model surrogates using machine-learning methods to replace very time-intensive simulations. Design an efficient training strategy for these machine-learning tools, making use of existing model simulations
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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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temporal codes. To ensure that these advanced models do not become opaque “black boxes,” we will integrate post-hoc explainability tools such as SHAP values (SHapley Additive exPlanations) Thrust C
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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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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
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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: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use machine learning (ML) along with data from previously solved problem
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trains thereby moving towards analyses that are sensitive not just to firing rates but also precise timing relationships underpinning temporal codes. To ensure that these advanced models do not become
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Jülich, which is dedicated to pushing the boundaries of data science theory and application. Our research spans from use-inspired, method-driven theory to application-driven research. Please find more
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our research both in front of and behind the scenes. What you will do Our applied research focuses on the following topics: Through our close cooperation, we combine basic research, application-oriented