NVH ML Engineer Intern: Tyre Noise Prediction

Nissan Motor Iberica SA; Palau-solità i Plegamans; Hybrid; EUR 6,700 - 11,000; Part time

Benefits offered by this job

Financial compensation during the/in t

Hybrid working

Thesis/project opportunity

Job summary

Nissan Motor Iberica SA offers a hands-on internship in Vehicle Noise Testing & Machine Learning for Trainee, based in Barcelona. You will support the development of a predictive model to understand tyre noise using test parameters and help optimize our RAG application parameters.

You will analyse data, build ML models, and design an interface to simulate tyre noise under different conditions, contributing to NVH digitalisation.

Qualifications

• 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).

Responsibilities

• 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.

Skills

Machine Learning concepts

Python programming

Mathematical modelling

Data analysis

English proficiency

Education

Bachelor's degree

Master's degree

Tools

Python

Scikit-learn

Job description

Nissan Motor Iberica SA offers a hands-on internship in Vehicle Noise Testing & Machine Learning for Trainee, based in Barcelona. You will support the development of a predictive model to understand tyre noise using test parameters and help optimize our RAG application parameters.

You will analyse data, build ML models, and design an interface to simulate tyre noise under different conditions, contributing to NVH digitalisation.