Do you want to contribute to improving human health?

This is a rare opportunity to join collaborative research at the intersection of computer science and medicine. We lead the way for how AI can be leveraged to predict and improve the outcome for women with breast cancer.


Your mission
We seek an outstanding recent (or imminent) PhD who have done research on the application of machine learning on medical images. Your main mission would be in extending our current breast imaging algorithms to a multi-modality and/or time-series setting. Our current mammography-based models would be extended to include MRI and ultrasound images. Repeated images over time would be used to improve the prediction of therapy response for women in treatment. In addition, we would include you in external collaborations and expect you to take responsibility for data management and exchange.


Your profile
You should have performed research in machine learning applied to medical images. Qualified to be a postdoctor is one who has obtained a doctorate or has equivalent scientific competence. Applicants who have not completed a doctorate at the end of the application period may also apply, provided that all requirements for a completed degree are met before the (intended) date of research start. This must be substantiated by the applicant's main supervisor, director or equivalent.


What do we offer?
A creative and inspiring environment full of expertise and curiosity. Our collaborative group between Karolinska Institutet and the Royal School of Engineering has produced several influential research pieces including the assessment of several commercial algorithms and developing our own algorithms for predicting the individual risk of breast cancer and for predicting how difficult it would be for a radiologist to correctly identify cancer in a given image. Currently, we conduct two clinical studies that are on the cutting edge internationally – one where AI is used to detect breast cancer for women in one screening center and another where AI is used as a selection method for MRI.

At Karolinska Institutet, we conduct successful medical research and hold the largest range of medical education in Sweden. Karolinska Institutet is a state university, which entitles to several benefits such as extended holiday and generous occupational pension. Employees also have access to our modern gym for free and receive reimbursements for medical care.

Division and Location:
The affiliation is with the Department of Oncology-Pathology in professor Jonas Bergh’s research group. The work place in the Fredrik Strand research team is located at the Breast Imaging unit at the Karolinska University Hospital in Solna.

Research team Fredrik Strand
Your career continues here]


Entry requirements
Scholarships for postdoctoral qualification can be established for foreign researchers who place their qualifications in Sweden. The purpose of scholarships for postdoctoral qualification is to promote internationalization and contribute to research qualification after a doctorate or equivalent.

A scholarship for carrying out postdoctoral research can be granted for a maximum of two years within a four-year period following the receipt of a doctoral degree or equivalent.
To be eligible for a postdoctoral scholarship, the person must have obtained a doctorate or a foreign degree deemed to be equivalent to a doctorate. Applicants who have not completed a doctorate at the end of the application period may also apply, provided that all requirements for a completed degree are met before the (intended) start date of the post-doctoral education.

The head of the department determines whether their previous training and scholarly qualifications correspond to a Swedish doctorate or higher.


Other requirements
You should have performed research in the field of machine learning applied to medical images.


Additional desirable qualifications

  • Having published as first author, at least two peer-reviewed original research articles in a journal with an impact factor of 5 or higher, or an equivalent conference paper (e.g., ICLR, NeurIPS, CVPR, ICCV, ICML)
  • Deep learning frameworks as desirable skills: TensorFlow, PyTorch, CUDA
  • Experience in Linux, Python and using Docker containers
  • DICOM images in your research, especially from mammography and/or MRI
  • Management of large medical image datasets
  • Knowledge of statistical methods applied to medical research
  • Data sharing and knowledge of GDPR (European Union)
  • Fluency in the English language in oral and written communication
  • Good collaborative skills


Type of scholarship
The amount is tax free and it is set for twelve months at a time, paid out on a six months basis. In exceptional cases, shorter periods may be acceptable.


Contract type:
Full-time. One-year stipend (with extension possibilities).
First day of research: November 1 (with some flexibility).


Application process
An application must contain the following documents in English or Swedish:

A complete curriculum vitae, including date of the thesis defence, title of the thesis, previous academic positions, academic title, current position, academic distinctions, and committee work A complete list of publications A summary of current work (no more than one page)

The application is to be submitted on the Varbi recruitment system.


Want to make a difference? Join us and contribute to better health for all

First day of employment November 1 (with some flexibility).
Reference number STÖD 2-3835/2021
Contact
  • Fredrik Strand, fredrik.strand@ki.se
Union representative
  • Andreas Lundqvist, SACO, andreas.lundqvist@ki.se
  • Helen Eriksson, OFR, helen.eriksson@ki.se
Published 06.Sep.2021
Last application date 26.Sep.2021 11:59 PM CEST

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