PhD Candidate: Deep Learning for Phase-Contrast Synchrotron X-ray Tomography | PhD Position: Deep learning for phase-contrast synchrotron X-ray tomography

Job Summary

This PhD position is part of the ErUM-Data project CmarT at the Helmholtz Zentrum Hereon, based at the DESY campus in Hamburg. The successful candidate will work at the forefront of imaging science, developing a novel multi-scale imaging approach for synchrotron radiation facilities. Day-to-day responsibilities include developing physics-informed, self-supervised learning approaches (such as deep image priors and GANs) for phase retrieval and implementing reconstruction algorithms on High-Performance Computing (HPC) clusters. You will bridge the gap between micro- and nano-tomography to visualize soft tissues and hierarchical structures in materials. This role is highly collaborative, involving close work with beamline scientists and international project partners. It offers a unique opportunity to combine machine learning with high-end experimental physics at one of the world's fastest nano-tomography setups, supported by a 3-year contract and the benefits of the German public service collective agreement.