Vehicle Noise Testing & Machine Learning Engineer, Trainee

Are you passionate about automotive innovation, machine learning, and AI? Ready to apply your skills to real-world engineering challenges in a global company? Nissan is looking for a proactive and curious intern to join our NVH (Noise, Vibration & Harshness) Team.

Support the development of a predictive model to better understand and estimate tyre noise based on key test parameters such as:

• Tyre pressure / stiffness / Vehicle speed / Ambient temperature / and other relevant vehicle and test conditions.

To optimize the internal RAG application adding new technical parameters based on the history vehicle development key data.

Using Machine Learning and AI tools, your goal will be to:

• Analyse and process vehicle test data.
• Develop predictive models to estimate tyre noise behaviour.
• Investigate the influence of different tyre and vehicle parameters on exterior noise performance.
• Build an intuitive interface that enables engineers to simulate tyre noise based on different tyre specifications and operating conditions.
• Contribute to the digitalisation and optimisation of NVH engineering processes.

• Strong knowledge of Machine Learning and AI concepts and tools.
• Programming skills in Python or similar languages.
• Solid understanding of mathematical modelling and data analysis methods.
• Interest in applying technology to real engineering challenges.
• Fluency in English (spoken and written).

🎓 To join this internship experience, you must be currently enrolled in a Bachelor's or Master's Degree and able to establish a university internship agreement with your academic institution.

At Nissan, we believe that the differences among us – differences in race, gender, age, mindset, religion, people with disabilities and much more – make us stronger and equip us to better serve our customers and communities. We are committed to creating positive change that ensures transparency and inclusivity throughout the career journey, in order to build teams that create a sense of belonging for all members.