Postdoc in Shape Theory and Image Reconstruction
The research group in imaging within the Department of Mathematics at KTH-Royal Institute of Technology is offering a two-year postdoc position based on a grant from the applied mathematics programme at the Swedish Foundation for Strategic Research. The position is part of a larger medical imaging project where the overall goal is to develop theory and algorithms for image reconstruction applicable to x-ray based medical imaging with under-sampled and/or highly noisy data. Imaging modalities involved are 3D spiral/helical CT, 4D SPECT/CT and PET/CT, C-arm 3D-CT, and spectral CT. The project also includes applications to x-ray and electron microscopy phase contrast imaging. Overall clinical goals are to significantly reduce the total dose of x-rays and/or acquisition time while maintaining a clinically useful image quality, alternatively to significantly improve image quality given a fixed total dose/acquisition time.
The research associated with the position includes development of theory and implementation of numerical algorithms for joint non-rigid image registration and reconstruction for tomographic inverse problems. Specific focus is on deformable templates using diffeomorphisms for deforming images by means of a group action, such as LDDMM and metamorphosis, and their connections to optimal transport. Another topic is to apply the aforementioned theory to spatiotemporal imaging, i.e., to reconstruct a 3D template and its time evolution from a time series of tomographic data. The large-scale nature of the problems requires algorithms that are efficiently implemented with small memory footprint. Implementations of algorithms will be as software components integrated with ODL, a Python-based software framework for numerical functional analysis. Part of the research may include close collaboration with the medical technology company Elekta and with clinicians at Karolinska University Hospital in Stockholm.
There is a possibility to teach at 20% if the candidate wishes to do so.
We seek a candidate with a strong background in differential geometry and functional analysis. A PhD degree, awarded (or planned to be awarded) before the commencement of the position, in mathematics is a requirement. The candidate must also have experience from software development in scientific computing, preferably using Python and/or C/C++. Experience from optimization on Riemannian manifolds and image processing/reconstruction is highly beneficial but not required. Finally, a successful applicant must be strongly motivated, have the capability to work independently as well as in collaboration with members of the research group, and have good communication skills.