Intern/Thesis – Learning-based Radar-Camera Fusion for Simultaneous Localization and Mapping
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Job Description
CARIAD automotive software intern researching radar-camera fusion and robust SLAM for autonomous vehicles. Developing state estimation, localization, and 3D mapping methods through experiments and analysis.
Responsibilities:
- Independently investigate novel approaches for radar-camera fusion
- Develop and evaluate methods for robust state estimation, localization, and/or 3D map generation
- Design experiments and compare approaches against state-of-the-art methods
- Analyze results and derive insights for multi-modal SLAM algorithms
- Work with the ground truth generation team and PhD researchers on radar-camera fusion and robust SLAM research for autonomous vehicles
- Contribute to research on radar-camera state estimation, robust localization, consistent map generation, and cross-modal alignment between vision and radar data
Requirements:
- Excellent academic performance
- Enrolled student in Computer Science, Robotics, Electrical Engineering, Mathematics, or equivalent
- Know-how of relevant sensors for autonomous driving and measurement technology
- Programming knowledge and experience in Python and/or C++
- Confident in English in both oral and written form
- Open-minded team player passionate about self-driving technologies and solving hard problems
- First research experience, such as internships, projects, papers, or coding competitions, is a plus
Benefits:
- Remote work options within Germany
- Flexible remote working by agreement
- Diversity and inclusion support
- Application assistance for candidates with disabilities
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