Master Thesis Data-Driven Microstructure-Bases Material Modelling (f/m/x)

Bayerische Motoren Werke Aktiengesellschaft; München; Hybrid; EUR 60.000 - 80.000; Vollzeit

Zusammenfassung

A leading automotive company offers a Master Thesis opportunity in Data-Driven Microstructure-Based Material Modelling in Munich. You'll support innovative vehicle development by working with finite element methods and LS-Dyna for polymer foams. Ideal candidates are studying mechanical engineering or related fields, possess strong programming skills, and have good English proficiency. The role includes excellent scientific support, mobile work options, and student apartments subject to availability.

Leistungen

Mobile work

Apartments for students (subject to availability)

Qualifikationen

• Must be supervised by a university for the thesis.
• Deep understanding of finite element method required.
• Ideally knowledge in data-driven material models.
• Courage and motivation to contribute ideas is important.
• Enjoyment of supporting an international team is needed.

Aufgaben

• Support implementation of data-based material model.
• Help create an LS-Dyna user material for polymer foams.
• Contribute to comparison of test and simulation results.
• Gain insight into process setup for introducing models.

Kenntnisse

Finite element method

Data-driven material models

Programming in Python

Programming in Matlab

Team collaboration

Very good English skills

Ausbildung

Studies in mechanical engineering

Studies in materials science

Studies in physics

Tools

LS-Dyna

Jobbeschreibung

Master Thesis Data-Driven Microstructure-Based Material Modelling (f/m/x)

THE BEST INTERNSHIP IN THEORY - AND IN PRACTICE.

SHARE YOUR PASSION.

World-leading technologies don’t make it into a BMW until they’ve undergone one of the most challenging journeys imaginable. It takes dynamic teams with outstanding technical skills to take them from the drawing board to the road. That’s why our experts will treat you as part of the team from day one, encourage you to bring your own ideas to the table – and give you the opportunity to really show what you can do.

Our team at the BMW Group is dedicated to advancing data-based material modelling for innovative vehicle development. We focus on integrating new material models into full-vehicle simulations and offer you the opportunity to support our simulation experts in the field of battery-powered vehicles with your thesis.

What awaits you?

• You will support the implementation of a data-based material model based on microstructure models.
• Furthermore, you help to create an LS-Dyna user material for polymer foams.
• In addition, you contribute to the comparison and correlation of test and simulation results.
• Moreover, you will gain insight into the process setup for introducing data-based material models in LS-Dyna for complete vehicle simulation.
• You will receive excellent scientific support and be part of our motivated, cross-functional team.

Please note that your thesis must be supervised by a university on your part.

What should you bring along?

• Studies in mechanical engineering, materials science, physics or a comparable field.
• Deep understanding of the finite element method, ideally with LS-Dyna experience.
• Ideally, knowledge in data-driven material models.
• Programming skills in Python or Matlab.
• Courage and motivation to contribute your own ideas and explore new paths.
• Enjoyment of supporting an international team.
• Very good English skills; proficiency in German is a plus.

Would you like to support our team in advancing data-based material modelling for innovative vehicle development? Then apply now!

What do we offer?

• Mobile work.
• Apartments for students (subject to availability & only at the Munich location).

Do you have questions? Then submit your inquiry easily via our contact form. Your inquiry will be answered by phone or email afterwards.

We at the BMW Group place great importance on equal treatment and equal opportunities. Our recruiting decisions are based on the personality, experiences, and skills of the applicants. More about this here .