Postdoctoral Position in Trustworthy Machine Learning in Cancer at the Department of Electrical and Information Technology of Lund University, Sweden

 

This position is partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) and the CanFaster program (Marie Skłodowska-Curie COFUND program).

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.

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CanFaster is an EU Marie Skłodowska-Curie COFUND program on cancer research and entrepreneurship. The goal of CanFaster is to educate tomorrow’s cancer researchers in a broad set of scientific, transferable, and business skills in order to develop the mind-set of an entrepreneur. During this call, the program will recruit the last five postdoctoral fellows out of 15 in total. The program is administrated by CREATE Health, located in the energetic and dynamic science park, Medicon Village. Lund University and the Life Science research in the Medicon Valley area has been nominated as one of 13 Brain belts in the world.

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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 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 the 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.

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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

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Should you be interested in this position (CanLearn: Trustworthy Machine Learning in Cancer Treatment and Care Pathways), please see more details here.