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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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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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, and responsiveness across care settings. Within care homes, these issues are exacerbated by staff shortages, fragmented communication. As a result, despite the growing use of telehealth systems such as
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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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this limitation in the use of satellite observations by make a direct use of radiance observations retrieved by satellites using machine learning without the need of radiative transfer calculations. The new model
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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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central role in this process. We are looking for a Research Assistant / Doctoral Student in applied Biotechnology or Chemistry (m/f/d) for the SuReMo research project on the Development of sustainable
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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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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