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
Master's thesis: Application of deep learning methods to 3D LiDAR environment data

Winter Semester 2025/26 – Fixed term for 6 months

 

YOUR TASKS:

  • Develop intelligent algorithms for demanding outdoor applications based on 3D LiDAR data
  • Explore state-of-the-art deep learning methods for 2D/3D environment perception (segmentation, object detection and classification)
  • Train deep learning models and evaluate various algorithms in terms of accuracy and efficiency
  • Work with cutting-edge 3D LiDAR sensors and gain hands-on technical experience
  • Assess the applicability of deep learning methods on modern AI accelerators such as NVIDIA Jetson and Hailo
  • Collaborate closely with engineers to develop innovative solutions
  • Document your results in a structured and traceable manner

 

YOUR PROFILE:

  • You are currently pursuing a master’s degree in computer science, physics, electrical engineering, mathematics or a related field
  • You enjoy diving into new and challenging topics and developing novel solutions
  • You have solid programming skills, ideally in C++ or Python
  • You have initial experience with deep learning and frameworks such as TensorFlow or PyTorch
  • You work in a systematic and structured manner
  • Creativity in problem-solving and a passion for innovation round off your profile
Information at a Glance
Apply now
Requisition-ID:  37053
Posting Job Location:  Hamburg-Rahlstedt
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.