Internship/ Master Thesis on developing tracking methods for deformable objects (f/m/x)

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 silence 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. With us, you have the opportunity to perfectly combine your practical studies while participating in 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

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 methodologie

Demonstrable experience applied 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