In interaction with the professors and research management team at itec, the candidate co-supervises and further develops the AI and machine learning research line within the different application domains, and carries out pioneering research him/herself. This will involve collaboration across the disciplines within itec, and may also involve collaboration with companies (hospitals, training organizations, as well as technology companies). In this context, the candidate will co-supervise a number of PhD students in AI and machine learning, and will interact with post-docs in the other disciplines at itec (in particular in statistics). The candidate is expected to actively participate in the recruitment of projects, and to publish the results of the research in scientific journals and present them at international and local conferences.

The candidate will interact with researchers active in health and education. In the health domain, we are primarily focusing on clinical decision support systems. These tools often involve risk prediction models for medical conditions, where both accuracy and interpretability are of high importance. In the domain of education and training, we focus on decision support for learners and teachers/experts, and develop novel techniques for recommending learning tasks matched to the skills and interests of learners, for automated assessment of learning tasks that involve multiple skills (e.g. essays), and for predicting dropout in learning environments that require high self-regulation (e.g. MOOCs). In this domain, the candidate interacts with researchers in imec’s Smart Education research programme, both within itec and with research groups at the universities of Ghent (UGent) and Brussels (VUB), as well as with imec. One research direction that is important in both application domains is time-to-event prediction (survival analysis), which is on the border between statistics and machine learning, and will be of great importance for this function.