Thesis - Uncertainty-Aware ML for Vehicle Dynamics

We are Geely Technology Europe. A unified European R&D centre within Geely Auto Group, where world-class engineers, developers and

innovators push the boundaries of intelligent mobility. Our mission is to shape the next generation of vehicle architectures,

digital technologies and intelligent systems for global markets. We integrate European customer and regulatory requirements early

in the development process and support multiple brands within the Geely portfolio, including Zeekr, Lynk & Co and Geely. With

nearly two decades of engineering experience in Europe, we continue to build smart, sustainable and user‑centric mobility

solutions.

Automated driving and driver-assistance functions need to know how much performance the vehicle has left in each situation, its

capability envelope. Planning and control algorithms usually rely on simplified models and fixed limits, which can be either too

optimistic or too conservative. High-fidelity, physics-based simulation and reachability analysis capture these limits accurately

but are far too computationally expensive to run on board. This is an open problem at the intersection of machine learning,

simulation and control, how to learn fast and reliable models from expensive simulations, quantify their uncertainty, and adapt

them to real-world data.

The thesis takes the vehicle's performance limits from high-fidelity simulation to a real-time, self-adapting, data-driven model

ready for assisted and automated-driving functions.

• Simulation-based data generation: design efficient sampling strategies, such as design of experiments or active learning, to

build training data from high-fidelity models.

• Machine Learning-based modelling and uncertainty quantification: learn fast data-driven models of the limits and quantify how

confident they are.

• Online learning and adaptation: update the model from real vehicle data so it tracks changes over time and bridges the

sim-to-real gap.

The thesis is expected to demonstrate that a high-fidelity capability envelope can be made deployable, adaptive and useful for

assisted and automated-driving functions, with following deliverables

• Ongoing Master’s degree in Data Science, Machine Learning, Complex Adaptive Systems, Vehicle Engineering, Systems and Control,

or a related field

We are engineers, developers, and innovators from around the world. Joined together by entrepreneurship, our unique blend of

global culture, and a belief in a smarter more sustainable future. At Geely Tech Eu we fast-track innovation and transform ideas

into pioneering technology solutions, doing your master thesis here is no different. We are convinced that a thesis project is a

major contribution to our innovation capabilities and long-term development. You'll have a great opportunity to use your skills

and creativity to push the boundaries of what´s possible.

What happens when you apply

If this sounds interesting and you match the requirements, please don't hesitate to submit your application with a CV and cover

letter. Shortlisted candidates will be contacted for an interview to further discuss the project's details and expectations.

Supervisor: Karthik Prasad, Expert Motion Systems, karthik.prasad@zeekrtech.eu, please contact for more information about the

project

Please note that due to GDPR regulations, we can only accept applications sent through the recruitment system, not via email or

other channels.