Master thesis - Embodiment-Aware Autonomous Driving Policies with Deep Learning

Job description** The goal of this thesis is to investigate the learning of vehicle-configuration-aware driving policies using imitation learning and embodiment conditioning. The assignment is divided into several subtasks: - Develop or extend a motion-planning policy capable of incorporating vehicle-configuration information. - Evaluate transfer-learning strategies for adapting policies from passenger vehicles to heavy-duty vehicle configurations. - Investigate how driving data from multiple vehicle configurations can be combined within a unified learning framework. - Implement, train, and evaluate the proposed solutions in terms of their generalization capabilities, data efficiency, and driving performance. **Education/program/focus** Master (civilingenjör) in computer science, robotics, electrical engineering, or applied mathematics, preferably with specialization in artificial intelligence algorithms. Number of students: 1-2 Start date: January 2027 Estimated time needed: 20 weeks **Contact persons and supervisors** Caroline Skoglund, Industrial PhD Student (KTH Robotics, Perception and Learning) [caroline.skoglund@scania.com](mailto:caroline.skoglund@scania.com) Yunus Emre Sahin, Research & Development Engineer in Autonomous Motion [yunus-emre.sahin@scania.com](mailto:yunus-emre.sahin@scania.com) Hiring Manager: Magnus Granström, [magnus.granstrom@scania.com](mailto:magnus.granstrom@scania.com) **Application:** Your application must include a CV, cover 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. References \[1\] K. Bousmalis et al., “RoboCat: A self-improving generalist agent for robotic manipulation,” Trans. Mach. Learn. Res., 2024. \[2\] A. Gupta, L. Fan, S. Ganguli, and L. Fei-Fei, “MetaMorph: Learning universal controllers with transformers,” in Proc. Int. Conf. Learn. Representations (ICLR), 2022. \[3\] H. Oh and J. Park, “MVAdapt: Zero-shot multi-vehicle adaptation for end-to-end autonomous driving,” 2026. \[Online\]. Available: [https://arxiv.org/abs/2604.11854](https://arxiv.org/abs/2604.11854) Publication date: 1.10.2026 - 30.11.2026 (applications evaluated continuously) Requisition ID: 33767 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/1443732633/?locale=en_US)