Internship: Fast Physics

Damen’s Research, Development & Innovation (RD&I) department develops and implements the technology and know-how to support the company’s ambition to become the world’s most sustainable and digitally connected shipyard. The department supports the business in creating an innovative product portfolio and provides forward-thinking guidance to improve the quality and performance of Damen’s products and services.

You will join the Data Science team within Damen RD&I in Gorinchem. The department applies cutting-edge data and AI solutions to shipbuilding and maritime operations, with expertise in physics-informed machine learning, simulation acceleration, predictive maintenance, computer vision, and operational analytics.

This internship is part of a strategic project focused on accelerating complex simulations for ship performance using machine learning and graph-based AI.

About the role

As an intern, you will work on the Fast Physics project, which aims to drastically reduce the runtime of high-fidelity computational fluid dynamics (CFD) simulations of ship hulls. These simulations are essential for predicting how a vessel behaves in water, but they can take hours to compute.

Instead of running time-consuming physics-based simulations, the project uses geometric deep learning, a type of machine learning that can learn from vessel designs and quickly estimate results such as water resistance or flow around the hull. The outcome is a working prototype that can support early-stage design exploration and simulation optimization.

You will contribute to enhancing the performance of an existing system that predicts physical quantities, such as ship resistance and flow fields, based on geometry and operating conditions. Your primary focus will be on a dedicated topic involving the training, validation, and extension of the framework to support multiple ship types and/or varying levels of simulation fidelity. The assignment can be a thesis or graduate internship and could start from September onwards.

Key accountabilities

You will be responsible for the following aspects:

• Support the 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.
• Work closely with the team together with Data Scientists, naval architects, and external partners such as MARIN.
• Document results and present findings to the team regularly.

Skills & Experience

We are looking for a student who:

• Is currently pursuing a Bachelor or Master in Mechanical Engineering, Applied Mathematics, Computer Science, Data Science, or a related technical field.
• Has experience with Python, and ideally deep learning frameworks such as PyTorch or TensorFlow.
• Has familiarity with 3D geometry formats or CFD simulation and numerical data.
• Has a strong interest in physics-based modeling and applying AI to engineering problems.
• Communicates fluently in English.

What we offer

• Mentoring at academic level throughout the internship.
• Internship/graduation fee and travel allowance for the duration of the assignment.
• Opportunity to contribute to a high-impact innovation project in collaboration with leading maritime companies, institutes, and universities.
• Research publication is likely possible, with a possible extension of the internship period.
• Possibility to visit partner hubs or research centers, such as MARIN in Wageningen, depending on project needs and availability.

Due to housing issues we cannot accept international students that do not have accommodation in the Netherlands yet.