Master thesis in the field of efficient Deep Learning

• Design and implementation of efficient Birds Eye View (BEV) based segmentation and planning models for autonomous driving
• Build robust deep learning architectures capable of processing high-resolution images, improving the prediction performance of BEV perception models
• Identify and address bottlenecks in current BEV research, pushing forward the boundaries of BEV-based perception for autonomous applications
• Optimize hyperparameters and network architectures using various optimization algorithms
• Documentation and presentation of the results

• Currently enrolled in a Master’s program in Data Science, Computer Science, Computational Science, Mathematics, or a related field
• Very good to good academic achievements
• Strong ability to comprehend, analyze, and present research papers within BEV and related domains
• Proficiency with PyTorch, CUDA, and other relevant deep learning frameworks
• Strong programming skills in Python and/or C#, with experience in CI/CD pipelines to streamline development
• Ability to dive in and work independently on a scientific topic
• Previous research experiences in the field of BEV perception and 3D Computer Vision is an advantage
• German and English Language Level B2

Previous research experiences in the field of BEV perception and 3D Computer Vision

• Opportunity to participate in the research and development of intelligent algorithms in a young and interdisciplinary team
• Help shape the mobility of tomorrow
• Up to 40% remote working