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quantitative field, ideally with a strong focus on computational practice Strong mathematical and statistical background, with pronounced analytical and problem-solving skills Proven programming expertise in
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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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) Use co-regulation networks for gene function and protein–protein functional relationship prediction (guilt-by-association), and benchmark them against existing bulk co-expression resources Compare and
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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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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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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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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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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