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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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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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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
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an online application form in which you can enter your application data. This is what you have to do: 1. Register in the DAAD portal (Read notes about registering in the portal >> ) 2. Request recommendation