Master thesis - AI uncertainty to guardrails

Job description** The work is primarily a structured literature review, complemented where useful by lightweight computational experiments. Training a new driving policy or implementing a complete safety stack is outside the scope. The student will: - Review uncertainty definitions and sources in learned driving-policy outputs. - Compare methods for estimation, representation, calibration, and evaluation. - Study how uncertainty can guide safety margins, constraints, interventions, and fallback decisions. - Recommend directions for future research on uncertainty-aware safety supervision. The work will be conducted with researchers and engineers at TRATON and KTH. **Education/program/focus** Master's student in Systems, Control and Robotics, Computer Science, Electrical Engineering, Engineering Physics, Applied Mathematics, or a related field. Experience in machine learning, probabilistic methods, autonomous driving, robotics, planning and control, or literature reviews is beneficial. Number of students: 1 Start date for the thesis work: \[To be agreed\] Estimated time required: 20 weeks, full time (30 credits) **Contact persons and supervisors** Pedro Lima, [pedro.lima@scania.com](mailto:pedro.lima@scania.com); Hiring Manager: Jon Andersson, [jon.andersson@scania.com](mailto:jon.andersson@scania.com) **Application** Your application must include a CV, personal letter, and transcript of grades. A background check might be conducted for this position. We are conducting interviews continuously and may close the recruitment earlier than the date specified. Publication date: 1.10.2026 - 30.11.2026 (applications evaluated continuously) Requisition ID: 33763 Number of Openings: 1.0 Part-time / Full-time: Full-time Permanent / Temporary: Temporary Country / Region: SE Location(s): Södertälje, SE, 151 38 Required Travel: 0% Workplace: Hybrid [Apply now »](https://jobs.scania.com/talentcommunity/apply/1444187833/?locale=en_US)