Thesis project

We are Zeekr Technology Europe. Powered by engineers and creative minds from all over the world. Located in Gothenburg, Sweden, one of the leading mobility innovation grounds worldwide. Established in 2013 as CEVT and began with the mission to create a revolutionary modular vehicle architecture, CMA. As of 2024, CEVT is now Zeekr Technology Europe, signaling our belonging within Zeekr. Delivering intelligent, sustainable, and user centric technology and software for tomorrow's electric mobility today.
Project Description
Virtual sensors are transforming how complex vehicles states are estimated that are hard or costly to measure. By fusing physics knowledge with learning-based models, Physics-Informed Neural Networks (PINNs) can deliver accurate, physically consistent estimates while staying data-efficient. Deploying such models at the edge enables low-latency, safety-compliant, and robust operation directly on embedded automotive hardware. This thesis explores PINN-driven virtual sensors to enhance real-time vehicle dynamics control without adding hardware complexity.

Scope
This project will design and evaluate edge-deployable virtual sensors powered by PINNs for vehicle state estimation across diverse conditions. It will integrate vehicle dynamic models and constraints within neural architectures, prioritize lightweight inference for embedded targets, and emphasize transferability with minimal retuning. The work covers concept design, model development, and edge deployment, supported by simulation and available datasets.

Objectives

• Design a PINN-based virtual sensor that embeds core vehicle dynamics and constraints.
• Enforce physical feasibility and stability through structured loss terms and bounded outputs.
• Achieve real-time inference on edge hardware with compact, efficient model designs.
• Demonstrate accuracy, generalization, and robustness across operating regimes with minimal adaptation.
• Validate against reference signals and representative baselines using clear, comparable metrics.

Expected Outcomes

• A working virtual sensor prototype deployed on an embedded edge target with real-time performance.
• Physically consistent estimates that remain robust under varying conditions with limited data.
• Clear trade-offs between accuracy, latency, and compute footprint suitable for engineering decisions.
• Evidence of model transferability and adaptability with minimal calibration.
• Reusable assets, including code, models, deployment scripts, and concise documentation.

Your Skills And Background

• Ongoing Master's degree in Computer Science, Physics, Complex Adaptive Systems, or relevant fields with specialization in Machine Learning
• Strong background in Deep Learning (CNN, RNN, LSTM, GAN, etc.)
• Experience with Machine Learning for Time-series or Dynamical systems
• Experience with Machine Learning frameworks (e.g., TensorFlow, PyTorch)
• Knowledge in Control systems, Vehicle dynamics or Applied physics
• Strong programming skills in Python, C/C++, or similar languages
• Familiarity with embedded or real-time computing constraints
• Strong analytical and problem-solving skills

This thesis project is for 2 students who together need to have the required skills and background. Applicants may specify a preferred partner in their cover letter.

Why you should join Zeekr Tech Eu
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 Zeekr 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, , please contact for more information about the project

Starting date:
January 2026

Last application date:

Apply today. We will perform ongoing selection during the application period. We look forward to hearing from you

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