Perception Intern

Summary

Work closely with experienced professionals in their chosen field to apply what is learned in the classroom to real-life work situations. Under supervision, they will assist team members or directly perform day-to-day departmental functions and projects. They will gain an enhanced understanding of their field and the automotive manufacturing industry, laying the foundation for a successful career. 
 

Responsibilities

• Research, benchmark, and test implementations of state-of-the-art deep learning models for LiDAR-based object detection and segmentation.
• Assist in improving LiDAR detection performance, particularly for complex surfaces, through literature review and experimental evaluation.
• Support the implementation and evaluation of deep learning models for LiDAR point clouds (e.g., PointNet, PointNet++, and other 3D architectures).
• Contribute to the development and assessment of perception algorithms that combine LiDAR and camera data under supervision.
• Help collect, preprocess, and analyze camera and LiDAR data for model development and testing.
• Experiment with open-source toolkits such as Open3D and PCL for point cloud processing and visualization.
• Assist in creating scripts and small pipelines for data preparation and result analysis (using Python, NumPy, OpenCV, etc.).
• Participate in code reviews, documentation, and team meetings to learn best practices in research and development.
• Stay current with recent advancements in LiDAR perception and deep learning by reading and summarizing relevant scientific papers.

The above description represents the most significant essential duties of this position but does not exclude other occasional work assignments not mentioned.

Qualifications

• Current enrollment in a Bachelor’s, Master’s, or Ph.D. program in Computer Science, Electrical Engineering, Robotics, or a related field.
• Coursework or project-based experience in computer vision, deep learning, or robotics.
• Familiarity with at least one deep learning framework (e.g., PyTorch or TensorFlow).
• Understanding of fundamentals in data analysis, 3D geometry, and point cloud processing.
• Exposure to LiDAR data and/or related libraries (Open3D, PCL).
• Programming experience in Python. Familiarity with C++ is an advantage.
• Ability to conduct literature surveys and summarize technical concepts accessibly.
• Strong problem-solving and communication skills, and enthusiasm for hands-on learning in a collaborative team environment.