Postdoctoral in Depth Learning for Medical Image Analysis Royal Institute of Technology, School of Computer Science and Communication

Postdoctoral in depth learning for medical image analysis


Royal Institute of Technology, School of Computer Science and Communication

 

KTH is one of Europe's leading technical universities and an important arena for knowledge development. As Sweden's largest university of technical research and education, we gather students, researchers and faculty from around the world. Our research and education covers both natural sciences and all branches of technology as well as architecture, industrial economics, social planning, technical history and philosophy.

 

For more information about the School of Computer Science and Communication, visit www.kth.se/csc.
department Information

 

The postdoctoral position will formally be located at the Department of Computational Science and Computing, CST, at KTH, but the practical work will be performed at the Science for Life Laboratory. CST conducts computer science research and modeling in biological and physical systems. This requires efficient and advanced algorithms as well as image analysis systems. For more information about ongoing work, see https://www.kth.se/csc/forskning/cst .

 

SciLifeLab is a national center for molecular biosciences focusing on research in health and the environment. The center combines leading technical expertise with advanced knowledge in translational medicine and molecular biosciences. Our goal is also to build a strong research group around SciLifeLab through education and collaboration. SciLifeLab is a national resource run by Karolinska Institutet, KTH, Stockholm University and Uppsala University. We cooperate with several other Swedish universities. For more information, visit https://www.scilifelab.se/pa-svenska/ .

 

duties

 

This position is part of a collaboration with a doctor from Karolinska University Hospital. The main task is to develop deep learning methods for analyzing medical images focusing on breast cancer. The successful applicant will apply his / her knowledge of deep learning to several types of medical images, including histological sections, mammograms and possibly others. In general, the goal is to predict patient outcomes, but we aim to develop models for specific predictors of patient outcomes, such as biomarkers for tumor heterogeneity and risk models. In addition to these medical applications, the successful candidate will also participate in theoretical research in deep learning and computer vision.

 

The position is initially funded for one year, with the possibility of renewal depending on funding and eligibility.

 

qualifications

 

The candidate must have a doctorate in computer science, computational science or a related field obtained within three years before the end of the application period. Proven knowledge and ability in one or more deep learning frameworks (Tensorflow, Keras, Torch, Caffe, etc.) is absolutely necessary. It also requires knowledge of common computer vision techniques and experience in implementing, analyzing and optimizing scientific image analysis applications. Knowledge in one or two scientific computational languages (Python, Matlab, R) is required. Experience of parallel programming environments and cloud computing is a plus. Past experiences that work with medical or biological images are also desirable.

 

Union representatives

 

You will find contact information for union representatives on the KTH website .

 

Application

 

You apply through KTH's recruitment system. You as the applicant have the primary responsibility for your application being complete when it is submitted.

 

The application must be KTH at the latest midnight CET / CEST (Central European Time / Central European Summer Time) deadline.

 

The application must contain the following documents:

 

  1. Information about interest, including a brief description of the experience of deep learning
  2. Curriculum vitae
  3. Transcript from the university
  4. Reference Contact
  5. Representative publications (or other example of scientific writing)

 

Please note that all material must be in English in addition to official documents.

 

Other

 

We discourage direct contact with staffing and recruitment companies as well as sales adverts.

 

Type of employment Fixed-term employment longer than 6 months


Extent of employment Full-time


Access According to the agreement


Salary Monthly Salary


Number of vacancies 1


employment rate 100%


place Solna


County Stockholm County


Country Sweden


References D-2017-0814


Contact
Kevin Smith / Bitr University Lecturer,, ksmith@kth.se, +46 8 790 64 37


Maria Engman / HR administrator, maengm@kth.se


Published 2017-11-14


Application deadline 2018-01-06


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