# Submission #243

No

Industry

Post-doctoral fellowship on Image Reconstruction/Deep Dictionary Learning (S-2017-1165)

Short Term

Department of Mathematics, KTH Royal Institute of Technology

Medical Technology

http://www.math.kth.se

ozan@kth.se

Stockholm

Sweden

This 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.

Further information along with instructions for how to apply are given in the link below:

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

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.

Further information along with instructions for how to apply are given in the link below:

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

35000

Swedish krona

Fri, 12/01/2017