Summary
KTH Royal Institute of Technology is offering a doctoral student position focused on 3D dynamic scene understanding for autonomous systems. The project aims to enhance scene flow estimation through self-supervised, multi-modal models combining LiDAR geometry with camera semantics.
Doctoral Student in Perception for Dynamic Scene Understanding, Stockholm County, Sweden
Designation
Doctoral Student in Robotics, Perception, and Learning
Research Area
- 3D Dynamic Scene Understanding
- Scene Flow Estimation
- Autonomous Systems
- Machine Learning
- Computer Vision
Location
- Stockholm County, Sweden
Eligibility/Qualification
| Requirement | Description |
|---|---|
| Education | Master’s degree or equivalent (second cycle degree) |
| Credit Hours | Minimum 240 higher education credits, including 60 second-cycle credits |
| English Proficiency | Equivalent to English B/6 |
| Skills | Programming, implementation of machine learning methods, collaboration, analytical skills |
Job Description
The doctoral student will develop models that generalize motion representations for autonomous planning, addressing the limitations of current methods. The position emphasizes independent research, collaboration, and applying machine learning within the context of computer vision.
How to Apply
To apply for this position, candidates should submit:
- Copies of diplomas and grades from previous university studies
- CV detailing relevant professional experience
- A cover letter (max 1 page) including:
- Reasons for pursuing research studies
- Description of relevant project experience or comparable skills
- Relation of interests and skills to the project
- List of publications or technical reports with links to full texts
Applications must be submitted through KTH’s recruitment system and should be complete as per the advertisement instructions.
Last Date to Apply
- May 15, 2026 (Applications must be received by midnight, CET/CEST)
This opportunity provides a platform for innovative research in one of Europe’s leading technical universities, supported by the Wallenberg AI, Autonomous Systems, and Software Program (WASP).
