PhD Scholarship in Machine Learning for Non-Stationary and Non-Static Nano-Tomography - DTU Compute

Recent advances in machine learning and deep learning are opening new possibilities for computational imaging, including the reconstruction of complex 3D structures from experimental imaging data. In this fully funded PhD project at DTU Compute, you will develop novel learning-based methods for X-ray nano-tomography.

As state-of-the-art synchrotron imaging systems push towards ever higher spatial resolution, even minute positioning errors, mechanical drift, and sample deformations become sources of image degradation. The central challenge is to reconstruct accurate 3D and 4D representations of samples while accounting for positional uncertainties during image acquisition and changes in sample shape over time.

The project will bring together neural representations, inverse problems, geometric modeling, and computational imaging to address fundamental challenges in nano-tomography. Rather than correcting acquisition errors and sample deformations as separate preprocessing steps, you will investigate how to model and estimate the sample structure, acquisition geometry, and deformation jointly from the measured data.

The position is based at DTU Compute and conducted in close collaboration with EPFL and the Paul Scherrer Institute (PSI) in Switzerland, one of the leading centers for synchrotron X-ray imaging1 …