Patric Jensfelt

PhD opportunity at KTH Royal Institute of Technology in 3D dynamic scene understanding , with a focus on scene flow estimation , robotics , perception and learning , and autonomous systems . The project addresses a key challenge in modern perception: making 3D motion understanding generalize across different sensors and environments, while also supporting the safety and decision-making needs of autonomous systems. The research direction is strongly oriented toward self-supervised and multi-modal learning , including methods that combine LiDAR geometry and camera semantics to learn robust motion representations. The position is hosted by the Department of Robotics, Perception and Learning at KTH and is linked to the Wallenberg AI, Autonomous Systems and Software Program (WASP) . The student will also be connected to the WASP graduate school. The starting point for the project is the OpenSceneFlow codebase and related research on self-supervised scene flow. Funding: fully funded PhD position with monthly salary according to KTH’s doctoral student salary agreement. Eligibility highlights: a second-cycle degree (for example a master’s degree) or equivalent, or at least 240 higher education credits including 60 second-cycle credits; English equivalent to English B/6; strong ability to program and implement machine learning methods, preferably in computer vision; and good independent, collaborative, and analytical skills. Application: apply through KTH’s recruitment system. The application must include diplomas and grades, proof of language requirements, CV, a one-page motivation/application letter, and a list of publications or technical reports. Deadline: 2026-05-15 .

A PhD position is open at KTH Royal Institute of Technology in the area of perception for dynamic scene understanding , with a strong focus on 3D vision , scene flow estimation , robotics , machine learning , and autonomous driving . The project is based in the Department of Robotics, Perception and Learning and aims to develop a self-supervised, multi-modal model that combines LiDAR geometry and camera semantics to learn motion representations that generalize to unseen real-world scenarios. The work is connected to the group’s prior research on self-supervised scene flow and is intended to support autonomous planning and safety-critical perception. The position is funded by the Wallenberg AI, Autonomous Systems and Software Program (WASP) , and the student will be connected to the WASP graduate school . The announcement mentions access to strong compute resources through NSC and a research environment with an active community and colleagues. Eligibility: applicants must have basic eligibility for doctoral studies, typically a master’s degree or equivalent, and must meet the English requirement equivalent to English B/6 . The selection process also values the ability to work independently and collaboratively, a professional approach, strong English communication, and experience programming and implementing machine learning methods, preferably in computer vision. Application: submit the application through KTH’s recruitment system with diplomas, transcripts, proof of language requirements, CV, a motivation letter, and publications/technical reports. The deadline is 2026-05-15 .