Student Assistant Computer Vision/Deep Learning

The Fraunhofer-Gesellschaft (www.fraunhofer.com) is one of the world's leading organizations for application-oriented research. 74 institutes develop pioneering technologies for our economy and society – more precisely: 30 000 people from technology, science, administration and IT. They know: Anyone who comes to Fraunhofer wants to and can make a difference. For themselves, for us and for the markets of today and tomorrow.

Future. Discover. Together.
The Computer Vision & Graphics group of the Vision & Imaging Technologies (VIT) department is looking for a student assistant in the area of deep learning for scene analysis in the construction domain/synthetic data creation and transformation. The group develops AI methods for 3D model creation, plan and scene analysis in the construction sector by analyzing as-built data (plans, images, point clouds) with artificial intelligence and integrating them into a BIM-based model. Become a part of our team and join us on our journey of research and innovation!

Be part of change

Phase and instrument recognition in laparoscopic image data for the automated identification of surgical steps, as well as for the detection and segmentation of anatomical and surgical landmarks, using modern deep learning approaches and novel training concepts
Analysis of hyperspectral image data for tissue and object classification, as well as for the reconstruction and spatial refinement of hyperspectral information through the combination of different model architectures
Development and integration of 3D reconstruction methods for the analysis of medical stereo-endoscopy data, including camera calibration, depth estimation, and geometric modeling of surgical scenes

What you contribute

You are pursuing a master’s degree in computer science, mathematics, physics, or an equivalent field at a university or college in Berlin with above-average grades
You have very strong programming skills in Python and are proficient in using deep learning frameworks such as PyTorch
You have a deep theoretical understanding of machine learning and deep neural networks.
You have programming skills in C++ (including OpenCV).
You are familiar with the fundamentals of computer vision and digital image processing.
You enjoy analytical thinking.
You can work in a structured manner, both independently and as part of a team.

What we offer

Fascinating challenges in a scientific and entrepreneurial setting
Attractive salary
Modern and excellently equipped workspace in central location
Great and cooperative working atmosphere in an international team
Opportunities to write a master's thesis
Flexible working hours
Opportunities to work from home

The position is initially limited to 1 year. An extension is explicitly desired.

The monthly working time is 80 hours. This position is also available on a part-time basis. We value and promote the diversity of our employees' skills and therefore welcome all applications – regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability. Our tasks are diverse and adaptable – for applicants with disabilities, we work together to find solutions that best promote their abilities. Remuneration according to the general works agreement for employing assistant staff.

With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future.

Ready for a change? Then apply now and make a difference! Once we have received your online application, you will receive an automatic confirmation of receipt. We will then get back to you as soon as possible and let you know what happens next.

If you require further information about this position, please contact:

Anna Hilsmann

anna.hilsmann@hhi.fraunhofer.de

Niklas Gard

niklas.gard@hhi.fraunhofer.de

Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute HHI

www.hhi.fraunhofer.de

Requisition Number: 85480 Application Deadline: 10/15/2026

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