As a student, you work with your colleagues on an equal footing and create ideal conditions for your future career.

Step out of your comfort zone, excel and redefine the limits of what is possible. That’s just what our employees are doing every single day – in order to set the pace through our innovations and enable outstanding achievements. After all, behind every successful company are many great fascinating people.

In a spacious modern setting full of opportunities for further development, ZEISS employees work in a place where expert knowledge and team spirit reign supreme. All of this is supported by a special ownership structure and the long-term goal of the Carl Zeiss Foundation: to bring science and society into the future together.

Join us today. Inspire people tomorrow.

Diversity is a part of ZEISS. We look forward to receiving your application regardless of gender, nationality, ethnic and social origin, religion, philosophy of life, disability, age, sexual orientation or identity.

Apply now! It takes less than 10 minutes.

Your role

With us, you have the opportunity to perfectly combine your studies with practical experience while actively contributing to exciting projects. This allows you to gain valuable skills, expand your network, and grow both professionally and personally.

• Familiarize with the state-of-the-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 and robotics

• Validation of the results with test measurements

• Evaluation of the technical feasibility

• Documentation of the experimental outcomes & test results

Your profile

• A background in either computer science, robotics engineering, or electrical engineering and currently enrolled in a master’s degree program

• Strong experience with programming in Python

• Good theoretical background in linear algebra, optimization, and computer vision methodologies

• Demonstrable applied experience with computer vision and deep learning libraries (e.g. 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

Contact

Franziska Gansloser