Master Thesis AI-Based Keypoint Refinement for Autonomous Driving at Bosch Group

Hildesheim, Lower Saxony, Germany -

Full Time

Start Date

Immediate

Expiry Date

01 Jul, 26

Salary

0.0

Posted On

02 Apr, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Computer Vision, Deep Learning, Python, PyTorch, Feature Matching, Multi-view Geometry, SLAM, Neural Network Architecture, Autonomous Driving, Research, Data Analysis, Benchmarking

Industry

Software Development

Description

Company Description At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference. The Robert Bosch GmbH is looking forward to your application! 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. Need further information about the job? Matthias Neuwirth-Trapp (Functional Department) [email protected] Work #LikeABosch starts here: Apply now! #LI-DNI Legal Entity: Robert Bosch GmbH

Responsibilities

The candidate will research and develop deep learning methods to improve keypoint matching accuracy for autonomous driving applications. They will design, implement, and benchmark neural network architectures to replace conventional post-processing chains with real-time capable solutions.