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Job Description

• During your thesis, you will research and develop advanced deep learning methods to improve keypoint matching accuracy for autonomous driving applications.
• You will design a unified neural network architecture that jointly addresses correspondence refinement, outlier rejection, and uncertainty estimation, aiming to replace complex conventional post-processing chains.
• Furthermore, you will investigate novel approaches to achieve sub-pixel precision and handle ambiguous image textures within the feature matching pipeline.
• You will implement and train these models, focusing on efficiency and the potential for distilling the architecture into a fast, real-time capable solution.
• Finally, you will benchmark your approach against current state-of-the-art matchers (e.g., in Visual Odometry scenarios) to demonstrate improvements in robustness and accuracy.

Qualifications

• Education: Master studies in the field of Computer Science, Robotics, Mathematics, Physics or comparable
• Experience and Knowledge: strong background in Computer Vision and Deep Learning; proficiency in Python and PyTorch; specific knowledge of feature matching, multi-view geometry, or SLAM is highly desirable
• Personality and Working Practice: you excel at taking ownership of your research topic with a self-driven, independent, and structured approach, developing your own creative ideas, and engaging in mature, professional technical discussions
• Work Routine: your on-site presence is required
• Enthusiasm: passionate about solving fundamental problems in geometric computer vision and AI
• Languages: very good in English

Additional Information

Start: according to prior agreement
Duration: 6 months
Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations and if indicated a valid work and residence permit.
Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.
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