Vehicle Noise Testing & Machine Learning Engineer, Trainee

Nissan Motor Corporation; Barcelona; Hybrid; EUR 10,000 - 15,000; Part time

Benefits offered by this job

Hands-on internship

Hybrid work model

Financial compensation

Thesis/project opportunity

Multicultural environment

Job summary

Nissan Motor Corporation in Barcelona is seeking a motivated student for a Vehicle Noise Testing & Machine Learning Engineer internship within the NVH Team. You will analyze tyre noise data, build predictive models, and explore how tyre and vehicle parameters affect exterior noise, while developing a user-friendly interface for simulations.

The role offers hybrid work, exposure to cutting-edge automotive tech, and a platform for Bachelor’s/Master’s final projects.

Qualifications

• Currently enrolled in a Bachelor’s or Master’s Degree and able to establish an internship agreement.
• Strong interest in applying ML/AI to real engineering challenges.

Responsibilities

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

Skills

Machine Learning concepts

Data analysis methods

Mathematical modelling

English fluency

Python programming

Tools

Python libraries (NumPy, scikit-learn)

Job description

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.

Your Mission

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.

We’re Looking For a Motivated Candidate With:

• 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).

What We Offer

• A hands-on internship with exposure to cutting-edge automotive technologies.
• The opportunity to work on a real project with direct impact on vehicle development.
• A multicultural and inclusive work environment with colleagues from around the world.
• Flexibility through hybrid working, combining remote work and collaboration at NTCE facilities.
• Financial compensation during the internship period.
• An excellent opportunity for students completing their Bachelor’s Final Project or Master’s Thesis.

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.

Barcelona Barcelona Spain