Postdoctor in Image Reconstruction/Deep Dictionary Learning
KTH Royal Institute of Technology, School of Engineering Sciences
KTH Royal Institute of Technology in Stockholm has grown to become one of Europe’s leading technical and engineering universities, as well as a key centre of intellectual talent and innovation. We are Sweden’s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as in architecture, industrial management, urban planning, history and philosophy.
The research group in mathematical imaging within the department of mathematics is offering a two-year postdoctoral fellowship 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. 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. Imaging modalities involved are 3D spiral/helical CT, 3D spatiotemporal 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.
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. Achieving the latter requires combining elements of l1-minimization with machine learning. An important aspect is to utilize structural information, e.g., in the form of existing anisotropic representation systems like curvelets and shearlets.
The research includes both theoretical development and implementation of numerical algorithms. The large-scale natures of the problems require algorithms that not only convergence fast but also have small memory footprint. Algorithms will be implemented as software components integrated with TensorFlow and ODL (http://github.com/odlgroup/odl), the latter is a Python-based software framework for numerical functional analysis. Part of the research may include close collaboration with the medical technology companies Elekta and Philips, and with clinicians at Karolinska University Hospital in Stockholm.
There is a possibility to teaching at 20% if the candidate wishes to do so.
A PhD degree in mathematics, signal processing or computational physics/engineering that has been awarded (or planned to be awarded) before the commencement of the position is a requirement. The candidate should have a strong background from machine learning or signal/image processing, the latter preferably in the context of tomographic image reconstruction. Experience from computational harmonic analysis, functional analysis, and sparse signal processing (compressive sensing) is highly beneficial. The candidate should also have experience from software development in scientific computing, preferably using Python and/or C/C++ in the context of machine learning. Finally, a successful applicant must be strongly motivated and have the capability to work independently as well as in collaboration with members of the research group.
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- A one-page cover letter, including a statement of your research experience and interests.
- CV and a publication list.
- Letter(s) of recommendation and/or names and contact details of 2-3 referees
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|Type of employment||Temporary position longer than 6 months|
|Contract type||Full time|
|First day of employment||According to agreement, preferably no later than August 1, 2018.|
|Number of positions||1|
|Last application date||01.Dec.2017 11:59 PM CET|