This position is part of a joint collaboration between the two largest research programs in Sweden, the Wallenberg AI, Autonomous Systems and Software Program (WASP) and the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS), with the ultimate goal of solving ground-breaking research questions across disciplines.
We are a translational group with a strong cross-disciplinary collaboration between breast radiology at Karolinska Institutet (KI) and computer science at the Royal School of Engineering (KTH) and SciLifeLab. The members at KTH are mainly responsible for the development of algorithms, and the members at KI are mainly responsible for directing the initial data curation and the later evaluation of algorithm performance. In addition, we are exploring aspects of AI governance, including setting up a national platform for retrospective validation of AI algorithms in mammography screening and to develop early-warning methods to detect when the AI predictions might no longer be trustworthy.
We now seek to strengthen our internationally leading clinical research group at KI with a postdoctoral researcher within the field of biostatistics applied to clinical studies of AI in breast cancer imaging.
Your research will consist of mainly two different topics. The first topic concerns the development and evaluation of in-house AI algorithms. One of your responsibilities will be to contribute with statistical competence in the development process of AI algorithms, helping the computer scientists with appropriate evaluation methods and with study design. You will also take responsibility for the retrospective evaluation of the developed algorithms in our data and in data from international collaborators. The second topic concerns the real-world follow-up of the performance and trustworthiness of AI algorithms in general, both in-house and commercial ones. You would evaluate different approaches to detect when a “black-box” AI algorithm may no longer be trustworthy, such as detecting distributional shifts. You might also explore the use of 3D-printed phantoms as reference standards for daily quality control of installed AI algorithms.
Qualified to be employed as 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 employment. This must be substantiated by the applicant’s main supervisor, director or equivalent.
We are looking for a person who is highly motivated, hard-working, self-directing and creative. You should have a PhD degree with focus on statistics, biostatistics, statistical machine learning, or a closely related field. Since we are working across disciplines and also have several international collaborators you should have excellent communication and collaboration skills. A track record of clinically oriented publications is considered an advantage. Experience of research in the cancer or imaging field would be especially beneficial. You should have experience with writing advanced scripts in a common statistical software package such as R, and be proficient, or willing to become proficient in Python scripting. Experience with a deep learning framework (TensorFlow, PyTorch, or JAX). You should have excellent communications and scientific writing skills.
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 better health for all. At Karolinska Institutet, we conduct innovative medical research and provide the largest range of biomedical education in Sweden. Karolinska Institutet is a state university, which entitles employees to several benefits such as extended holiday and a generous occupational pension. Employees also have free access to our modern gym and receive reimbursements for medical care.
The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) is a 12-year initiative that focuses on data-driven research, within fields essential for improving the people ́s lives, detecting and treating diseases, protecting biodiversity and creating sustainability. The programme will train the next generation of life scientists and create a strong computational and data science base. The program aims to strengthen national collaborations between universities, bridge the research communities of life sciences and data sciences, and create partnerships with industry, healthcare and other national and international actors.
Read more: https://scilifelab-2021.accomplice-dev.se/data-driven.