Master Thesis - Optimizing Sparse 3D Occupancy Prediction for Embedded Devices - REF5666W

Hey, you are looking for an exciting thesis and would like to gain valuable insights into the field of 3D Semantic Occupancy Prediction for Embedded Devices? Then we have just the thing for you!

We are looking for you starting in February for a period of 6 months to support us in our AI laboratory in Berlin. We will need you mostly on site in Berlin. But we also understand that there are days when you'd rather work from home. Therefore, together we find a solution that suits us all - remote working hours are therefore possible in consultation with your supervisor.

The following tasks are to be processed in this Master's thesis:

• You will actively work on sparsifying, pruning and quantizing CNN and transformer-based architectures for autonomous driving
• Design, implement, and evaluate models and training algorithms for runtime optimized 3D semantic occupancy predictions
• Summarize research findings in a paper/ master thesis and/or patent submission

While not mandatory, this position is particularly well-suited for students who are considering a future in research or pursuing a PhD.