Master Thesis - Scenario Generation for Autonomous Driving with Generative Models (f/m/d)
Master Thesis - Scenario Generation for Autonomous Driving with Generative Models (f/m/d)
Ingolstadt, DE, 85053 Berlin, DE, 10587 München, DE, 80807
Master Thesis - Scenario Generation for Autonomous Driving with Generative Models (f/m/d)
We are CARIAD, the automotive software company of the Volkswagen Group. Our teams build automotive software platforms and digital customer functions for iconic brands like Audi, Volkswagen, and Porsche – supporting the Volkswagen Group in becoming the leading automotive technology company. With CARIDIANS in Germany, the USA, China, Estonia, and India, we are transforming automotive mobility for everyone.
Join us and be part of this exciting journey!
YOUR TEAM
To support our "Scenario Generation" team, we are currently looking for a Master Thesis student, searching for new solutions to cope with the current challenges in autonomous driving specifically in the field of simulation and virtual verification. Aiming at developing future assisted and automated driving, a key challenge to enable self-driving vehicles are artificial algorithms and the related handling (gathering, storing, processing) of vehicle mass data for various situations and use-cases.
WHAT YOU WILL DO
- Support one of our PhD students in the field of Generative Models for generating challenging scenarios for Autonomous driving
- Summarize research literature related to your research project
- Conduct research with SOTA methods on Scenario Generation to evaluate existing AV planners and simulation frameworks working on large driving datasets
- Develop algorithmic ideas addressing open research challenges related to your topic in the field of generative simulation and policies for closed loop simulation and novel scenario generation
- Conduct comprehensive experiments on internal as well as public datasets.
WHO YOU ARE
- Enrolled student in computer science, automotive engineering, robotics, electrical engineering or similar field
- Solid theoretical and practical experience in advanced machine learning and Generative Models – Diffusion models, Flow Matching etc. is highly expected. Experience in the field of Reinforcement learning or implementations of popular research projects in the field is a nice to have
- Prior experience with simulation frameworks and open datasets is beneficial
- Proficiency in Python and deep learning frameworks (PyTorch, scikit-learn, Pytorch-Geometric)
- Structured and independent work, above-average commitment and flexibility is highly expected
- Fluency in written and spoken English
At CARIAD, we embrace individuality and diversity because we believe our differences make us stronger. We actively seek to build teams with a variety of backgrounds, perspectives, and experiences. Our goal is to create an environment where everyone feels valued and empowered to contribute. If you need assistance with your application due to a disability, please reach out to us at careers@cariad.technology - we are happy to support you.
We are CARIAD, the automotive software company of the Volkswagen Group. Our teams build automotive software platforms and digital customer functions for iconic brands like Audi, Volkswagen, and Porsche – supporting the Volkswagen Group in becoming the leading automotive technology company. With CARIDIANS in Germany, the USA, China, Estonia, and India, we are transforming automotive mobility for everyone.
Join us and be part of this exciting journey!
YOUR TEAM
To support our "Scenario Generation" team, we are currently looking for a Master Thesis student, searching for new solutions to cope with the current challenges in autonomous driving specifically in the field of simulation and virtual verification. Aiming at developing future assisted and automated driving, a key challenge to enable self-driving vehicles are artificial algorithms and the related handling (gathering, storing, processing) of vehicle mass data for various situations and use-cases.
WHAT YOU WILL DO
- Support one of our PhD students in the field of Generative Models for generating challenging scenarios for Autonomous driving
- Summarize research literature related to your research project
- Conduct research with SOTA methods on Scenario Generation to evaluate existing AV planners and simulation frameworks working on large driving datasets
- Develop algorithmic ideas addressing open research challenges related to your topic in the field of generative simulation and policies for closed loop simulation and novel scenario generation
- Conduct comprehensive experiments on internal as well as public datasets.
WHO YOU ARE
- Enrolled student in computer science, automotive engineering, robotics, electrical engineering or similar field
- Solid theoretical and practical experience in advanced machine learning and Generative Models – Diffusion models, Flow Matching etc. is highly expected. Experience in the field of Reinforcement learning or implementations of popular research projects in the field is a nice to have
- Prior experience with simulation frameworks and open datasets is beneficial
- Proficiency in Python and deep learning frameworks (PyTorch, scikit-learn, Pytorch-Geometric)
- Structured and independent work, above-average commitment and flexibility is highly expected
- Fluency in written and spoken English
At CARIAD, we embrace individuality and diversity because we believe our differences make us stronger. We actively seek to build teams with a variety of backgrounds, perspectives, and experiences. Our goal is to create an environment where everyone feels valued and empowered to contribute. If you need assistance with your application due to a disability, please reach out to us at careers@cariad.technology - we are happy to support you.
Ingolstadt, DE, 85053 Berlin, DE, 10587 München, DE, 80807