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: AI Meets 3D - Deep Learning Methods for Real-Time Logistics Applications

Summer Semester 2026 – Limited to 6 Months

 

Automation in logistics increasingly relies on the analysis of 3D data. A central application is the shape and object recognition of shipped goods. However, processing large point clouds presents a major challenge, as millions of measurement points must be reduced to the most relevant information. The aim of this thesis/internship is to research and develop deep learning methods for the efficient reduction and analysis of 3D point clouds. Real-world data and a practical application scenario will be available for evaluation.

 

YOUR TASKS:

  • Research current deep learning approaches for processing 3D point clouds and their use in shape recognition

  • Familiarize yourself with existing technologies for 3D data analysis

  • Develop methods to reduce point clouds to the most information-rich points

  • Conduct and evaluate experiments

  • Document your findings

 

YOUR PROFILE:

  • Degree program in Computer Science, Mathematics, or a comparable field

  • Programming experience in Python or C++

  • Knowledge of deep learning and 3D data processing

  • Good command of written and spoken English

  • Independent, structured, and reliable working style
Information at a Glance
Apply now
Requisition-ID:  37453
Posting Job Location:  Waldkirch (bei 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.