Postdoctoral fellowship in Image Reconstruction/Deep Dictionary Learning (S-2017-1165)
The 2-year 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.