Thesis in Deep Learning Environment Perception & Sensor Fusion with 3D ToF Sensors - Mobile Robots*
Thesis in Deep Learning Environment Perception & Sensor Fusion with 3D ToF Sensors - Mobile Robots*
Waldkirch (bei Freiburg), DE, 79183
Waldkirch (bei Freiburg), DE, 79183
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
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