UserAccount Thesis AI Meets 3D Deep Learning Methods for Real-Time Logistics Applications
UserAccount Thesis AI Meets 3D Deep Learning Methods for Real-Time Logistics Applications
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Job Description
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
Contact:Sarah Disch
Employment Type
Intern
