Intern: Fast Physics & Graph ML for Ship CFD

Damen Shipyards Group; Gorinchem; On-site; EUR 4,464 - 7,812; Part time

Benefits offered by this job

Academic mentoring

Travel allowance

High-impact innovation project

Research publication potential

Partner hub visits (MARIN Wageningen)

Job summary

Damen Shipyards Group seeks a motivated intern for its Data Science team in Gorinchem. You will work on accelerating CFD simulations using geometric deep learning to quickly estimate hull performance metrics, enabling efficient early-stage design exploration and optimization.

The role involves training and extending a framework to support multiple ship types and varying fidelity levels, with mentorship and opportunities for research publication and collaboration with MARIN and other partners.

Qualifications

• Pursuing a Bachelor or Master in Mechanical Engineering, Applied Mathematics, Computer Science, Data Science, or related technical field.
• Experience with Python and deep learning frameworks is a plus (PyTorch, TensorFlow).
• Familiarity with 3D geometry formats or CFD data and strong interest in physics-based modeling.

Responsibilities

• Support improvement of ML-based frameworks focusing on geometric deep learning and graph neural networks.
• Preprocess CFD simulation data and ship hull geometries.
• Run experiments in Python using PyTorch.
• Collaborate with Data Scientists, naval architects, and external partners (e.g., MARIN).
• Document results and present findings to the team.

Skills

Python

English proficiency

Team collaboration

Interest in physics-based modeling

Education

Bachelor or Master in Mechanical Engineering, Applied Mathematics, Computer Science, Data Science, or related

Tools

PyTorch

TensorFlow

3D geometry familiarity

CFD data processing

Job description

Damen Shipyards Group seeks a motivated intern for its Data Science team in Gorinchem. You will work on accelerating CFD simulations using geometric deep learning to quickly estimate hull performance metrics, enabling efficient early-stage design exploration and optimization.

The role involves training and extending a framework to support multiple ship types and varying fidelity levels, with mentorship and opportunities for research publication and collaboration with MARIN and other partners.