SICK is one of the world’s leading solutions providers for sensor-based applications in the industrial sector. Founded in 1946 by Dr.-Ing. e. h. Erwin Sick, the company with headquarters in Waldkirch im Breisgau near Freiburg ranks among the technological market leaders. With 63 subsidiaries and equity investments as well as numerous agencies, SICK maintains a presence around the globe. SICK has more than 10,000 employees worldwide and generated a group revenue of EUR 2.1 billion in the 2024 fiscal year.
Job Description
Thesis: Image-based object segmentation using AI accelerators on embedded camera systems

Winter semester 2026/27 – fixed term of 3–6 months

 

Precise assignment of detected features (e.g., barcodes) to the correct objects (e.g., parcels) is essential in logistics. Currently, only the positions of the features within the image are known – not the positions of the actual objects. This makes reliable assignment challenging, especially for objects located close to each other. To improve assignment accuracy, the object region in the image should be identified. By correlating the detected object region with the feature position, the reliability of the assignment can be significantly increased. The goal of this project is to integrate a demonstrator on an embedded camera system that can segment and localize individual objects in real time based on image sequences of material flows.

 

YOUR TASKS:

  • Commission a KI accelerator on an embedded camera system
  • Implement AI-based segmentation for object detection using the KI accelerator
  • Deploy a fully functional solution on the embedded camera system
  • Evaluate the developed solution in a real application environment

 

YOUR PROFILE:

  • Ongoing studies in Computer Science, Computer Vision, Machine Learning, or a comparable field
  • Basic knowledge of optics or physics
  • Programming experience in Python, MATLAB, Lua, or C++
  • Independent and structured working style, with a quick grasp of complex topics
  • Strong teamwork and communication skills
Information at a Glance
Apply now
Requisition-ID:  37735
Posting Job Location:  Waldkirch (near Freiburg)
Full-time/Part-time:  Full-time

Contact: Sarah Disch

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At SICK, we see people, not gender. 
We put great emphasis on diversity, reject discrimination and do not think in categories such as gender, ethnicity, religion, disability, age or sexual identity.