Thesis in Deep Learning Environment Perception & Sensor Fusion with 3D ToF Sensors - Mobile Robots*

Summer Semester 2025 - fixed-term for 3-6 months

YOUR TASKS:

• You design a system for 3D environment detection to avoid collisions of mobile robots, for example, in logistics applications.
• You conduct literature research and explore current deep learning methods for 2D/3D environment detection (segmentation, object recognition, and tracking) and sensor fusion.
• You process and fuse high-resolution RGB and depth data from real and simulated Time-of-Flight and RGB-D cameras.
• You develop algorithms for environment detection with the goal of avoiding collisions of mobile robots.
• Training deep learning models and evaluating various algorithms are part of your tasks.
• You integrate, optimize, and evaluate the systems on the current NVIDIA Jetson hardware.
• You document your work results in detail.

YOUR PROFILE:

• You are pursuing a master’s degree in computer science, robotics, mathematics, or a comparable field.
• You have solid knowledge of programming languages, ideally in C++ or Python.
• You possess a comprehensive understanding of image processing and handling 2D and 3D data.
• You have expertise in deep learning and machine learning, with initial experience in frameworks such as TensorFlow or PyTorch.
• Ideally, you have knowledge of robotics and robot applications, e.g., with ROS and NVIDIA Jetson.
• Your systematic and structured thinking and approach set you apart.
• Creativity in problem-solving and a passion for innovation complete your profile.

YOUR APPLICATION:

• We are looking forward to your online application
• Sarah Disch
• Job-ID 36414 
• All applications will be treated confidentially

*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.

Stichworte: Intern, Internship, Abschlussarbeit