Postdoc in CT-reconstruction with Deep Learning

Kungliga Tekniska högskolan, Skolan för teknikvetenskap

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 architecture, industrial management, urban planning, history and philosophy.

Job description

The research group in Mathematical Imaging within the Department of Mathematics is offering a two-year postdoctoral 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 development of algorithms that integrate physics models with machine learning for image reconstruction in spectral-CT. An essential part is to handle the non-linearity, another is to develop an appropriate statistical model for spectral-CT imaging. The clinical applications include cancer and stroke imaging. Prototype algorithms will be implemented as software components in ODL (http://github.com/odlgroup/odl), which is a python-based software framework for prototyping image reconstruction methods. Ideally one utilizes the couplings between ODL and several deep learning frameworks, like TensorFlow.

Much of the research will be pursued at MedTechLabs (https://www.medtechlabs.se) and at the Medical Imaging group at the Department of Physics, KTH. The group, which is led by Prof. Mats Danielsson, has developed novel photon-counting silicon strip detectors for CT-scanners that are now entering tests in a clinical setting at MedTechLabs. As a postdoctoral fellow, you will have access to clinical expertise and unique patient data from one of the first prototype spectral CT scanners in the world. You will also benefit from the strong research environments at KTH in mathematical sciences and medical imaging physics.

Qualifications

We seek a candidate with a PhD degree in mathematics, signal processing, computer science, or computational physics/engineering that has been awarded (or planned to be awarded) before the commencement of the position. The candidate should have a strong background from machine learning or signal/image processing, the latter preferably in the context of tomographic image reconstruction. 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.

Trade union representatives

You will find contact information to trade union representatives at KTH’s webbpage.

Application

Log into KTH’s recruitment system in order to apply to this position. You are the main responsible to ensure that your application is complete according to the ad.

Application

The following documents shall be included in the application:

  • CV
  • Cover letter
  • Research statement (2-3 pages)
  • Contact details for 2-3 references and/or corresponding support letters submitted directly by the writers by email to jobs@math.kth.se with S-2018-0728 in the subject line.

Your complete application must be received at KTH no later than the last day of application, midnight CET/CEST (Central European Time/Central European Summer Time).

Others

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Type of employmentTemporary position longer than 6 months
Contract typeFull time
First day of employmentAugust 15, 2019 or according to agreement
SalaryMonthly salary
Number of positions1
Working hours100%
CityStockholm
CountyStockholms län
CountrySweden
Reference numberS-2019-0728
Contact Ozan Öktem, +46 8 790 66 06, ozan@kth.se
Last application date30.May.2019 11:59 PM CET

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