Internship in Neural Camera-Projection Modeling

Are you eager to shape the future of computer vision Join us to develop a state-of-the-art data-driven and differentiable intrinsic camera-projection model that seamlessly integrates camera calibration directly into modern Deep-Learning systems.

• During your internship you will develop an innovative data-driven differentiable intrinsic camera-projection model alongside a dedicated methodology for its creation and application.
• You will implement this architecture as a differentiable neural network to facilitate end-to-end optimizable integration of camera calibration into downstream tasks.
• Additionally you will place a strong emphasis on Deep-Learning frameworks such as 3D-Reconstruction object detection and V-SLAM.
• Finally you will have the prospect of continuing this topic as a Masters thesis and publishing your developed methodology at a relevant conference.

Qualifications :

• Education: Master studies in a technical field like Computer Science or comparable
• Experience and Knowledge: in Machine Learning Linear Algebra and optionally in Computer Vision; first experience with Python (or equivalent programming language)
• Personality and Working Practice: you excel at structuring your tasks independently and proactively achieving your goals with strong personal drive
• Work Routine: presence in the office occasionally expected (at least once a month)
• Languages: very good in English

Additional Information :

Start: according to prior agreement
Duration: 3 - 6 months

Requirement for this internship is the enrollment at university. Please attach your CV transcript of records enrollment certificate 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
Paul Robert Herzog (Functional Department)
49 176

Work #LikeABosch starts here: Apply now!

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Remote Work :

No

Employment Type :

Full-time