PhD in Standard-Grounded Assurance of Adaptive Learning Systems (Mechanical and Industrial Engineering, Machine Learning, AI) Norwegian University of Science and Technology in Norway

The Department of Mechanical and Industrial Engineering at the Norwegian University of Science and Technology (NTNU) is inviting applications for a PhD position focused on the development of a theoretical framework for monitoring and updating adaptive learning systems, including machine learning and artificial intelligence systems, under formal assurance principles. The successful candidate will join the RAMS NTNU - Reliability, Availability, Maintainability and Safety Research Group, which is renowned for its research in reliability and safety engineering.

This PhD project aims to advance the assurance of adaptive learning systems by grounding the research in established standards and formal methods. The work will involve interdisciplinary collaboration across mechanical engineering, industrial engineering, and computer science, with a strong emphasis on machine learning, AI, and system safety. The candidate will have the opportunity to contribute to cutting-edge research at the intersection of engineering and artificial intelligence, addressing critical challenges in the reliability and safety of adaptive systems.

Applicants should hold a relevant master's degree in mechanical engineering, industrial engineering, computer science, or a related field. Experience or a strong interest in machine learning, artificial intelligence, and formal assurance principles is preferred. Good written and oral English skills are required. For more information on eligibility and application requirements, please refer to the official job posting.

For details on funding, stipend, and tuition coverage, consult the official job posting. The application process is managed through jobbnorge.no, where you can find further instructions and submit your application. The position offers an excellent opportunity to work in a leading research environment and contribute to the advancement of adaptive learning systems.