PhD in Artificial Intelligence/Machine Learning

The Department of Mechanical and Industrial Engineering invites applications for a PhD position focused on developing a theoretical framework for monitoring and updating adaptive learning systems (including machine learning/artificial intelligence systems) under formal assurance principles. The project aims to establish scientifically grounded mechanisms that enable learning systems to remain inspectable, verifiable, and trustworthy throughout their operational lifecycle./nAdaptive learning systems evolve through continuous updating, which introduces new challenges in reliability and assurance. Traditional engineering methods assume relatively static system behavior, whereas modern learning systems exhibit non-stationary performance over time. This research seeks to bridge that gap by developing a foundational framework that integrates monitoring and updating, along with standard-grounded assurance principles into a coherent theoretical structure./nThe PhD candidate will conduct fundamental research on how monitoring information can be used to evaluate adaptive learning system behavior in real time, how updating strategies can be formally constrained to preserve reliability properties, and how assurance concepts can be expressed in a way that is compatible with emerging standardization efforts. The emphasis is on mathematical modeling and quantitative indices, system-theoretic reasoning, and formal framework development rather than application-specific implementation./nLocation: Trondheim, Norway. Employer: Norwegian University of Science and Technology, Department of Mechanical and Industrial Engineering.