The department of Medical Informatics is looking for a PhD student to work on cutting-edge health AI and data science topics. This research will be performed in close collaboration with the Observational Health Data Sciences and Informatics (OHDSI) initiative, which is a global, multi-stakeholder, interdisciplinary collaborative to bring out the value of health data through large-scale analytics (
www.ohdsi.org) which develop frameworks to generate reliable real-world evidence.
As a PhD student, you will be responsible for the research on using federated data networks to improve best practices around the development and validation of prediction models. You will lead and contribute to projects conducting methodological research within the field of machine learning in healthcare. Research will be performed in clinical settings, feature engineering methods, deep-learning, and other advanced machine-learning methods to support personalized medicine. The research will focus on using large-scale federated data networks made possible by the standardization of health data to the OMOP Common Data Model. Impact assessments of these new approaches on patient care are part of the research agenda.