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the use of large language models to support neural network design and data preprocessing. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning
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denomination: post-graduates or academics who already have work experience and plan to do post-graduate studies or spend a period of research at a higher-education institution in Germany (2 to 6 months
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Infrastructure? No Offer Description Area of research: PHD Thesis Job description: Your Job: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use
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about PhD Graduate School. Prerequisite for the PhD position is an Master’s degree (or equivalent) in engineering, chemistry or physics. Do not overlook the inclusion of Academic Europe in your
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training strategy for these machine-learning tools, making use of existing model simulations and actively selecting new simulations where needed. Build and test a model “emulator” that can quickly explore
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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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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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of Economics and Business Administration with opportunities to complete a Master's degree course at a state (public) or state-recognised German higher education institution and to gain a Master's Degree in
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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