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particular, we aim to develop a neural network architecture that will allow us to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design
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, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular geometries. Current simulation-based approaches require complex 3D meshes and are often too slow
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Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular
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to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design‑space exploration, and on‑line operational optimization of power systems
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data sets, which have to be evaluated in order to obtain a holistic understanding of very complex systems. Visit HDS-LEE at: https://www.hds-lee.de/ The position is placed at the Institute for Advanced
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interdisciplinary research centers, offering access to world-class computational resources (HPC), state-of-the-art research software, and close integration with experimental and modeling groups across institutes and
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degree programme that has already started in Germany cannot be granted. If your Master's or postgraduate degree programme includes a study period abroad, funding for this period abroad is usually only
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on model behavior. We will divide our work into three thrusts: Thrust A: A first major objective will be to augment classical spike train analysis methods particularly those developed by Prof. Grün and
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are enrolled in a Master's or postgraduate degree programme which includes a study period abroad, funding for this period abroad is usually only possible under the following conditions: The study visit
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that has already started in Germany cannot be granted. If you are enrolled in a Master or postgraduate degree programme which includes a study period abroad, funding for this period abroad is usually