Internship/ Master Thesis on Computer Vision Applications for 3D Object Tracking

at  ZEISS Group

Your Role

We are seeking passionate and talented students who are eager to shape next-generation products at ZEISS. Integrated within a team of scientists and engineers, you will work on research topics in 3D computer vision and robotics. The project is based on high-precision object tracking involving subtasks such as pose estimation, semantic segmentation and so on leveraging both geometric and deep-learning methods from computer vision. Your role

• Familiarize with the state-of-art in pose estimation and tracking applications
• Development of the hardware experimental setup based on the use-case
• Implementation of prototype solutions relying on methods from both geometric and/ or deep learning methods in computer vision
• Validation of the results with test measurements
• Evaluation of the technical feasibility
• Documentation of the experimental outcomes & test results

Your Profile

• A background in the STEM area (computer science, robotics engineering, electrical engineering)
• Currently enrolled in a master’s degree program at a top university
• Prior experience with at least one programming language such as C++ or Python
• Good theoretical background in linear algebra, optimization, and computer vision methodologies
• Experience with CAD modelling software for 3D printing prototype objects will be beneficial
• Demonstrable applied experience with the computer vision (such as OpenCV, PCL, Open3D) and deep learning libraries (Tensorflow/ Pytorch) will be beneficial
• Self-motivated and independent working style along with a curiosity for diving into challenging topics that push the state-of-the-art

As a student, you will work on an equal footing with your colleagues, you will gain deep insights into a company that creates products for the world of tomorrow, and you will create ideal conditions for your later career
Your ZEISS Recruiting Team:
Franziska Ganslose

• Familiarize with the state-of-art in pose estimation and tracking applications
• Development of the hardware experimental setup based on the use-case
• Implementation of prototype solutions relying on methods from both geometric and/ or deep learning methods in computer vision
• Validation of the results with test measurements
• Evaluation of the technical feasibility
• Documentation of the experimental outcomes & test result