PhD in Robotics and AI for Engineering Asset Management

About the Role

Luleå University of Technology (LTU) invites applications for a funded four-year PhD position in the Division of Operation and Maintenance Engineering. The project investigates how robotics and artificial intelligence can improve predictive maintenance and asset management in heavy industrial environments, including mining and manufacturing. LTU’s proximity to LKAB, Boliden, and other major industrial partners makes this a hands-on, industry-connected research role.

All PhD students at LTU are employed, not enrolled on stipend. Monthly salary starts at SEK 30,500 and increases annually according to the state university pay scale.

Responsibilities

• Design and implement AI algorithms, primarily deep learning, for condition monitoring and remaining useful life prediction
• Build or adapt robotic inspection systems capable of operating in harsh underground or outdoor environments
• Collect and label sensor data in collaboration with industry partners
• Publish results in peer-reviewed journals and present at international conferences
• Contribute to at least one semester of undergraduate teaching as part of the doctoral employment

Requirements

• Masters degree in mechanical engineering, electrical engineering, computer science, or a closely related field (must be completed before the start date)
• Solid programming skills in Python or C++
• Experience with at least one machine learning framework (PyTorch or TensorFlow)
• Strong written and spoken English; Swedish is not required at entry but is encouraged

Nice to Have

• Hands-on experience with robotic platforms (ROS 2, embedded systems)
• Published research or a strong Masters thesis on robotics or predictive maintenance
• Familiarity with vibration analysis, thermal imaging, or acoustic emission sensors
• Some Swedish language proficiency or willingness to learn

How to Apply

Submit your application online through the LTU recruitment portal at ltu.se by 2 June 2026. Include a cover letter (one page, focused on research motivation), your CV, Masters thesis or preprint if available, degree certificates with grades, and contact details for two academic referees. Questions should be directed to the supervising professor listed on the vacancy announcement.