The Institute of Materials Physics operates instruments on large-scale equipment for structural investigations of materials and works on the development and characterization of novel lightweight materials for high-temperature applications, for example in aircraft turbines and automotive engines. This PhD project focuses on learning-based phase retrieval in the weak holographic regime, bridging the gap between micro- and nano-tomography. To address this, the PhD project combines machine learning, high-performance computing, and synchrotron-radiation experiments. The goal is to explore physics-informed self-supervised learning approaches (e.g., deep image priors, GANs) and iterative methods, combined with multi-scale tomography and local adaptive reconstruction to overcome these challenges. * develop physics-informed, self-supervised learning approaches for phase retrieval
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