Research Engineer in Deep Learning for Ultrasound Image Analysis - Hiring in process/Finished, not possible to apply

Do you want to contribute to top quality medical research in collaboration with KTH?

Welcome with your application to join Gynekologisk forskargrupp - Elisabeth Epstein Karolinska Institutet (ki.se), at Karolinska Institutet, Södersjukhuset in close collaboration with a Division of Computational Science and Technology at KTH Royal Institute of Technology.

The Department of Clinical Science and Education, Södersjukhuset, pursues undergraduate education, research education and internationally recognized research focusing on common diseases and injuries. The goal of all activities is to improve human health. The specific goal of our research team is to optimize the treatment of gynecological disease by skilled diagnostic management reducing anxiety, morbidity for the patient and minimizing the cost for the health care system.

The research group
The research in our team is signified by multi-disciplinary collaborations (with Sci-life lab, KTH Royal Institute of Technology and other clinical disciplines), spanning from cutting-edge translational research, innovative projects aiming to implement the use of Artificial Intelligence (AI) in the diagnostic triage, and clinical ultrasound studies improving the risk prediction for every woman in order to optimize and individualise management. Recently we were, to the best of our knowledge, the first to show that AI, using deep neural networks (DNN), applied on ultrasound images of ovarian tumours had a diagnostic accuracy on par with  human ultrasound experts. Our research has been awarded grants from Vetenskapsrådet, Innovationsfonden, Radiumhemmets forskningsfonder, Cancerfornden, ALF-medicin and ALF-MHT.
As part of your duties, you will work closely with AI researchers from KTH including the research groups of Kevin Smith and Pawel Herman.

Your mission
The candidate will be involved in our ongoing research project on deep learning based analysis of ultrasound images for computer assisted diagnosis of ovarian cancer. The main tasks of the candidate will be to conduct research, including a literature review, and perform technical tasks associated with the project including developing and deploying neural networks, curating data, etc. The candidate will need a strong technical background in machine/deep learning and competence in statistics. They should also have a sufficient ability to effectively communicate with medical doctors. As part of their duties, the candidate will likely be involved in the writing of scientific articles.

Your profil
The candidate is required to have a BSc degree or an advanced level (higher education) in the research subject or equivalent competence. An MSc degree is preferred.
The candidate should:

• be able to work independently
• have a strong background in mathematics
• be proficient in Python programming
• have experience with computer vision models
• be experienced with PyTorch and TensorFlow
• be familiar with a Linux environment, including shell scripting
• have strong technical writing, fluent English or Swedish

Great emphasis will be placed on personal competency.

What do we offer?
A creative and inspiring environment full of expertise and curiosity. Karolinska Institutet is one of the world's leading medical universities. Our vision is to pursue the development of knowledge about life and to promote a better health for all. At Karolinska Institutet, we conduct successful medical research and hold the largest range of medical education in Sweden. As a Research Engineer you are offered an individual research project, a well-educated supervisor. As a Research Engineer, you will receive monthly salary according to Karolinska Institutet’s policy. The time period is 1 year with a possibility to extend (subject to evaluation). We are open to negotiate either a part-time (50%) or full-time employment depending on the candidate’s preferences. Employees also have access to our modern gym for free and receive reimbursements for medical care.

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

Contact
For any questions in relation to the appointment you may contact Pawel Herman (paherman@kth.se), Kevin Smith (ksmith@kth.se), or Elisabeth Epstein (elisabeth.epstein@ki.se)