Summer Internship - Optimization & Control

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.

We are seeking a summer intern to join our Advanced Motion Systems team and contribute to the development of next‑generation optimization and control functionalities for embedded automotive applications.

Many real-world control problems rely on optimal control methods where the best action must be computed repeatedly as system conditions change. In practice, these computations can be too heavy for embedded platforms. In this internship, you will explore numerical solver and learning-based approaches to speed up these optimization steps while keeping performance and reliability high.

This position is well suited for students who enjoy combining mathematics, algorithms, and practical software implementation, and who want to apply their skills to real engineering problems under realistic embedded constraints.

What You Will Work On

You will develop and evaluate approaches for fast optimization on embedded systems, including:

• Understanding a selected optimal-control problem relevant to electric-vehicle operation (e.g., energy management and motion control).

• Formulating the optimization task at a high level and identifying what must be solved repeatedly during runtime.

• Implementing a baseline numerical approach and establishing performance metrics (speed, accuracy, robustness).

• Generating representative data by solving the optimization problem across relevant operating conditions.

• Investigating a lightweight approximation (e.g., a learned “fast minimizer”) that predicts the optimization result directly from system variables.

• Integrating the fast approach into a simulation loop and benchmarking it against the baseline method.

• Assessing practical considerations for embedded deployment (compute budget, numerical stability, failure modes, safety margins).

• Delivering a prototype implementation and summarizing findings in a short report and presentation to the team

Required Skills & Competence

We are looking for students with strong fundamentals in optimization/control and practical programming experience.

• Currently enrolled in or recently completed an MSc (or equivalent) program such as Applied Physics, Engineering Mathematics, Control/Systems Engineering, Computer Science, or a similar engineering discipline.

• Strong programming skills in Python, C/C++, and/or MATLAB.

• Experience in Numerical methods, Optimization, or Modelling/simulation workflows.

• Experience in Optimal control concepts (e.g., Pontryagin’s Minimum Principle) and Numerical optimization tools (e.g., CasADi).

• Knowledge in Embedded systems constraints (runtime, memory, robustness)

• Experience with Machine Learning models used as fast approximators/surrogates (e.g., neural networks).

• Domain Knowledge in Vehicle Dynamics, Motion Controls, Energy Management, etc.

• Strong analytical thinking and problem-solving abilities.

• Ability to communicate clearly and document work professionally.

• Comfortable working in an engineering team environment.

Please provide full transcripts along with your CV in your application

What You Gain

• End-to-end experience in building machine learning solutions for real automotive embedded systems.

• Exposure to practical constraints, including data quality, generalization, latency and compute limitations, and deployment trade-offs.

• Mentorship from engineers working at the intersection of vehicle dynamics, controls, and machine learning.

Internship Information

• Location: Gothenburg, Sweden. No relocation support is offered.

• Salary: Paid at a fixed hourly rate.

• Duration: You need to be available for a full-time internship. The period for the summer internship is between 1 June – 31 August. The start and end date are to be discussed with the recruiting manager.

• Requirements: You need to have a legal permit to live and work in Sweden.

For more information or questions please contact:
Supervisor: Karthik Prasad, Expert Motion Systems, karthik.prasad@zeekrtech.eu

Last application date: 2026-05-30

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.