Master Thesis in Multi-Modal Representation Learning in Autonomous Driving

Company Description

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.

The Robert Bosch GmbHis looking forward to your application!

Job Description

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 enhancing cutting-edge motion prediction models with novel and diverse contextual information.

In motion prediction for autonomous driving, backbone networks process input information in order to construct rich latent features with sufficient representation power. Recently, Foundation Models and Large Language Models have excelled at this task and shown tremendous promise in many autonomous driving applications. This is largely due to their multi-task nature and the ability to integrate language and image information. However, their application in motion prediction with the purpose of enhancing representation learning has been limited so far. This is especially important given that motion prediction models are increasingly being considered in a holistic manner with other tasks, i.e. in end-to-end pipelines. This thesis aims to investigate theoretically principled approaches to integrate rich multi-modal context information into state-of-the-art motion predictors.

• During your master thesis, you will collaborate with a team of engineers and researchers to design and implement advanced motion prediction backbones that integrate multiple sources of information.
• You will understand theoretical properties behind various approaches and use it to guide model development, e.g. from the perspective of latent variable models.
• In addition you will conduct experiments and analyze data to identify areas for improvement and optimize model accuracy and reliability.
• Stay up-to-date with the latest advancements in autonomous driving technology and contribute innovative ideas to the team.
• Last but not least, you will document findings and present results in a publishable manner as well as work on open-source benchmarks and datasets.

Qualifications

• Education: Master studies in the field of Computer Science, Electrical Engineering or comparable with a Robotics/Machine Learning focus and with very good grades
• Experience and Knowledge: in reading research papers and coding experience for machine learning applications, knowledge in Python with Pytorch, Tensorflow or JAX
• Personality and Working Practice: you are ready to learn a lot, in order to dive into a topic at the frontiers of machine learning research and autonomous driving applications and in case of potential own novel contributions, you should be open to publishing them
• Languages: fluent in English

Additional Information

Start: according to prior agreement
Duration: 6 months

Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations and if indicated a valid work and residence permit.

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.

Need further information about the job?
Janjos Faris (Functional Department)
+49 711 811 49109

#LI-DNI

Summary

• Type: Full-time
• Function: Research