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The Saarbrücken Graduate School of Computer Science provides an optimal environment for pursuing doctoral studies in computer science at an internationally competitive level. As a student, you
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‑space exploration, and on‑line operational optimization of power systems. Your tasks in detail: Become familiar with our previously developed neural network superstructure for learning iterative
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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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prediction Integration of domain decomposition methods into the learning framework to enable efficient model parallel training Implementation and optimization of GPU-accelerated training pipelines Validation
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efficient model parallel training Implementation and optimization of GPU-accelerated training pipelines Validation of models on patient-specific geometries obtained from MRI data Participation in conferences