Job Description

The Cooperative Autonomous Systems research group focuses on connected and autonomous vehicles and vulnerable road users. We research the latest technologies and methods to improve road safety. In this work we are also interested in the application of state-of-the-art perception models on vehicles. 

You can support us as a student assistant and learn about the latest technologies and get an insight into research.

The task include:

• Research on the popular existing computing hardwares used on existing vehicles (e.g. NVIDIA Xavier/Orin, Adreno GPU)
• Supporting research of existing (cooperative) perception machine learning models (e.g. BEVFormer) and their inference performance. 
• Optimizing the inference computation of ML models on target hardware, improving computation performance regarding inference speed and memory usage, so that the computation is practical. 
• Optimization methods include but not limited to: deep learning compiler such as TVM, JAX, utilizing SIMD, change of network architecture, quantization.

You should be motivated and have an interest in new technologies as well as the automotive industry. Previous knowledge of deep learning compiler, as well as basic hardware knowledge is an advantage.

What we offer:

• Flexible working hours
• Research related activities
• Contribution to publications
• Working with the latest technologies
• Coffee and great colleagues