PhD Candidate in Hybrid Learning-Control Methods for Autonomous Underwater Manipulation

Combine control theory and machine learning for autonomous underwater robotic manipulation at NTNU’s Department of Engineering Cybernetics and Norwegian Centre for Embodied AI. Directions include safe reinforcement learning, physics-informed ML, learning-based model predictive control and foundation models with performance guarantees. Work under Kristin Y. Pettersen and Jan Tommy Gravdahl. The doctorate lasts three years, with a possible teaching-related extension; daily presence in Trondheim is required.

Gross annual salary normally starts at NOK 580,000, with a 2% pension contribution. Submit the electronic Jobbnorge application by 16 October 2026.

Relevant master’s degree and control/cybernetics or marine-robotics preparation; normally a B-equivalent academic record. English evidence is required for applicants without a Scandinavian language or native English. Citizens of Norway, EU countries, Switzerland, Australia, Japan, New Zealand or NATO countries meet the stated equipment-access condition; other applicants must document ITAR eligibility. Include academic records, research motivation and the requested eligibility documents.