Ms. Thesis: Graph Neural Networks for Occupant Crash Simulation
Technology, Data & Digital · Data, AI & Analytics · Machine Learning · Data Science · Artificial Intelligence
In short
Responsibilities
- Develop and evaluate a transferable surrogate using an existing Autoliv dataset of sled simulations with crash test dummies (ATDs).
- Train a contact-aware GNN in one vehicle interior and evaluate its performance on an unseen vehicle interior.
- Predict ATD kinematics and interior contact in a sled environment.
- Quantify how well the GNN transfers to a new vehicle interior.
- Identify which outputs can be predicted reliably and which interior environments can be handled by the method.
Requirements
- Experience with Graph Neural Networks
- Experience with Machine learning
- Experience with Scientific computing
Desired Qualifications
- Interest in Automotive safety
- Interest in Finite element analysis
- Interest in Computational mechanics
Benefits
- Master Thesis (30 ECTS)
Skills
About Autoliv
Autoliv, Inc. is the worldwide leader in automotive safety systems. We develop, manufacture and market protective systems, such as airbags, seatbelts, steering wheels and pedestrian protection systems for all major automotive manufacturers in the world. Our products save 37,000 lives and prevent 450,000 injuries each year. Our ~65,000 employees in 25 countries are passionate about our vision of Saving More Lives and quality is at the heart of everything we do. We have 13 technical centers, with 20 test tracks. In the world of automotive occupant safety, we were the first to introduce the two-…
Company
AutolivJob Posted
2 days ago
Employment Type
Internship
Work mode
On Site
Experience Level
Entry
Locations
Vårgårda, Sweden
Gothenburg, Sweden
Qualification
Applicants
Be an early applicant
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