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CanLearn: Trustworthy Machine Learning in Cancer Treatment and Care Pathways

Principle investigator Amir Aminifar


The proposed project address crucial, specific questions related to trust in the decisions made by Artificial Intelligence (AI) and machine learning techniques in the context of cancer care pathways. Advanced machine learning frameworks will be considered and developed to ensure reliability in assisted decision making in cancer treatment and care pathways.


Over the past decades, we have been witnessing major breakthroughs in the Artificial Intelligence (AI) and machine learning domain, giving rise to many new opportunities and opening up new horizons. In particular, AI and machine learning techniques are actively being considered in the healthcare domain not only for precision decision making through algorithmic data analysis, but also to foster the efficiency with respect to resources, staff, time, and expenditures. Today, however, there is a lack of trust in the decisions made by the AI and machine learning techniques. In particular, it has been shown that the state-of-the-art AI and machine learning techniques may have weaknesses with respect to robustness and suffer from systematic biases. Therefore, the adoption of such techniques has to be with extreme care, particularly in the cancer treatment and care domain with so much at stake. This research project focuses on application and development of a framework based on AI and machine learning techniques for automated decision making in the context of cancer treatment and care pathways, while taking the trust element into account. Therefore, the postdoctoral fellow will be active in the trustworthy machine learning domain, investigating new techniques for reliable decision making in relation to cancer care pathways and treatment outcomes.


Key requirements:

  • Doctoral degree in health informatics, bioinformatics, or computer science and information technology, with focus on data analysis and machine learning, or similar
  • Excellent skills in bioinformatics and with machine learning tools, e.g., Python
  • Excellent written and oral proficiency in English
  • Strong publication record


Advantageous skills:

  • Knowledge in cancer treatment and care pathways
  • Previous experience in cancer treatment and care pathways, or similar
  • Knowledge or experience in trustworthy machine learning, adversarial and robust machine learning, interpretability/explainability, or similar
  • Ability to work in an interdisciplinary environment and in team
  • Ability and interest to take part in teaching activities
  • Experience in project management


Partners: Wallenberg AI, Autonomous Systems and Software Program (WASP) is Sweden’s largest individual research program ever, a major national initiative for strategically motivated basic research, education and faculty recruitment. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry.