About the job
Are you passionate about the future of autonomous driving? We are seeking a talented and motivated individual to join our team of experts dedicated to advancing the capabilities of autonomous vehicles. In this role, you will play a crucial part in using Reinforcement Learning (RL) to enhance the performance of end-to-end (E2E) approaches.
Responsibilities
During your Master thesis, you will collaborate with a team of engineers and researchers to bridge the gap between RL simulation and training, and E2E driving.
Furthermore you will understand the fundamental properties behind different training strategies and use them to guide the development of novel models and policies.
You will engineer and contribute efficient and high-performance software.
In Addition you will conduct experiments and analyze data to identify areas for improvement and optimize model accuracy and reliability.
You will stay up to date with the latest advancements in autonomous driving technology and contribute innovative ideas to the team.
Finally, you will document findings and present results in a publishable manner as well as work on open-source benchmarks and datasets.
Qualifications
Minimum
Education: Master studies in the field of Computer Science, Electrical Engineering or comparable with a Robotics/Machine Learning focus and very good grades
Experience and Knowledge: Reading research papers and programming experience for machine learning applications, with sound knowledge in Python, Pytorch, Tensorflow or JAX
Personality and Working Practice: you are ready to learn a lot and dive into a topic at the frontiers of machine learning research and autonomous driving applications; in case of own novel contributions, you should be eager to publish them
Work Routine: office attendance required
Languages: very good in English
Preferred
No preferred qualifications listed.