Internship: Fast Physics for Ship CFD & AI-Driven Design

Qabird; Gorinchem; On-site; EUR 12,276 - 16,740; Full time

Benefits offered by this job

Mentoring at academic level

Travel allowance

Internship/graduation fee

Publication potential

Visit partner hubs

Job summary

Damen RD&I in Gorinchem invites a summer intern to join the Data Science team. You will work on the Fast Physics project to drastically reduce CFD runtime using geometric deep learning, learning from vessel designs to estimate water resistance and flow around hulls.

You will contribute to training and extending the framework, preprocessing CFD data, and running Python experiments. Mentoring at academic level is provided, with potential for thesis extension and collaboration with MARIN.

Qualifications

• Pursuing a Bachelor or Master in Mechanical Engineering, Applied Mathematics, Computer Science, Data Science or related field.
• Experience with Python and DL frameworks such as PyTorch or TensorFlow.
• Familiarity with 3D geometry formats or CFD data.
• Strong interest in physics‑based modeling and applying AI to engineering problems.
• Fluent English communication.

Responsibilities

• Train, validate, and extend ML framework for multiple ship types and fidelity levels.
• Preprocess CFD simulation data and ship hull geometries.
• Run experiments in Python using PyTorch.
• Collaborate with Data Scientists, naval architects, and external partners.
• Document results and present findings to the team.

Skills

Python

PyTorch

TensorFlow

3D geometry

CFD data

English

Education

Bachelor or Master in Mechanical Engineering

Applied Mathematics

Computer Science

Data Science

Tools

None

Job description

Damen RD&I in Gorinchem invites a summer intern to join the Data Science team. You will work on the Fast Physics project to drastically reduce CFD runtime using geometric deep learning, learning from vessel designs to estimate water resistance and flow around hulls.

You will contribute to training and extending the framework, preprocessing CFD data, and running Python experiments. Mentoring at academic level is provided, with potential for thesis extension and collaboration with MARIN.