Postdoctoral Positions at the KTH Royal Institute of Technology - CCIMI - Cantab Capital Institute for the Mathematics of Information

The KTH Royal Institute of Technology welcomes applications for the below positions related to research on regularization for tomographic reconstruction, one on PET/SPECT and the other on combining deep learning with sparsity promoting regularization;

Postdoctor in PET/SPECT Image Reconstruction (S-2017-1166)
Deadline: December 1, 2017
Brief description:
The position includes research & development of algorithms for PET and SPECT image reconstruction. Work is closely related to on-going research on (a) multi-channel regularization for PET/CT and SPECT/CT imaging, (b) joint reconstruction and image matching for spatio-temporal pulmonary PET/CT and cardiac SPECT/CT imaging, and (c) task-based reconstruction by iterative deep neural networks. An important part is to integrate routines for forward and backprojection from reconstruction packages like STIR and EMrecon for PET and NiftyRec for SPECT with ODL (http://github.com/odlgroup/odl), our Python based framework for reconstruction. Part of the research may include industrial (Elekta and Philips Healthcare) and clinical (Karolinska University Hospital) collaboration.
Announcement & instructions:
http://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:158920/type:job/where:4/apply:1

Postdoctor in Image Reconstruction/Deep Dictionary Learning (S-2017-1165)
Deadline: December 1, 2017
Brief description:

The position includes research & development of theory and algorithms that combine methods from machine learning with sparse signal processing for joint dictionary design and image reconstruction in tomography. A key element is to design dictionaries that not only yield sparse representation, but also contain discriminative information. Methods will be implemented in ODL (http://github.com/odlgroup/odl), our Python based framework for reconstruction which enables one to utilize the existing integration between ODL and TensorFlow. The research is part of a larger effort that aims to combine elements of variational regularization with machine learning for solving large scale inverse problems, see the arXiv-reports http://arxiv.org/abs/1707.06474 and http://arxiv.org/abs/1704.04058 and the blog-post at http://adler-j.github.io/2017/07/21/Learning-to-reconstruct.html for further details. Part of the research may include industrial (Elekta and Philips Healthcare) and clinical (Karolinska University Hospital) collaboration.

Announcement & instructions:
http://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:158923/type:job/where:4/apply:1