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application deadlines) please visit https://www.qu.uni-hamburg.de/cluster/jobs/research-positions.html and klick on “+ Doctoral researchers”. The details of the application process (including starting dates
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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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, graph neural networks, physics-informed ML) to approximate PF results Train models using simulation results generated from conventional power flow solvers Evaluate AI-based approximators in terms
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to world-class science. In cooperation with the Johannes Gutenberg-Universität Mainz the LINAC department offers a PhD student position (all genders) in Accelerator Physics Reference number: 25.141-6680
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 19 days ago
. We believe that diverse perspectives drive innovation. Through strong partnerships, we accelerate the transfer of new ideas from the lab to real-life applications, improving lives. Your work provides
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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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, able to target HPC and the above novel architectures Your Profile: Master’s degree (preferably with subsequent PhD degree) in physics or a related field at the start date (ideally with a background in
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, including experiments at the local tandem accelerator facility as well as at national and international research infrastructures (https://ikp.uni-koeln.de/groups/wimmer/join-us ). YOUR TASKS » Participation
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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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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