Naval Damage Localization Intern: iFEM + Deep Learning

TNO; Delft; On-site; EUR 6,900 - 7,800; Full time

Benefits offered by this job

615 EUR monthly internship allowance

Jong TNO membership

Travel expense contribution

8 hours leave per month

Job summary

TNO invites motivated students in civil/marine/aerospace engineering to join a research internship focused on Structural Health Monitoring for naval applications. You will develop iFEM tooling to reconstruct displacement and stress fields, and explore ML approaches to identify damage locations under extreme loading.

Collaboration with the Naval and Offshore Structures Department is expected. The project combines literature, numerical analysis, and software development in Python, Abaqus/Ansys,

Qualifications

• Experience with finite element modelling (Abaqus/Ansys or similar).
• Interest in ship structures, numerical simulations and AI.
• Basic programming skills in Python.

Responsibilities

• Investigate and develop how structural damage can be localized after extreme loading.
• Develop iFEM tooling to reconstruct displacement and stress fields.
• Develop and validate a Deep Neural Network or ML method to identify damage locations.
• Validate the developed tooling and assess limitations of iFEM for SHM.
• Contribute to broader SHM research for maritime applications.

Skills

Numerical simulations

Python programming

Machine learning basics

Education

Civil/Marine/Aerospace engineering (final year)

Tools

Abaqus

Ansys

Job description

TNO invites motivated students in civil/marine/aerospace engineering to join a research internship focused on Structural Health Monitoring for naval applications. You will develop iFEM tooling to reconstruct displacement and stress fields, and explore ML approaches to identify damage locations under extreme loading.

Collaboration with the Naval and Offshore Structures Department is expected. The project combines literature, numerical analysis, and software development in Python, Abaqus/Ansys,