Damen aims to become the world's most sustainable and digitally connected shipyard. The Research, Development & Innovation (RD&I) department develops and implements the technology and know-how to achieve these ambitions. We actively assist the business in creating an innovative product portfolio and provide forward-thinking guidance to improve the quality and performance of Damen's products and services. You will be joining the Data Science team within Damen RD&I, located in Gorinchem . Our department focuses on applying cutting-edge data and AI solutions to Damen’s shipbuilding and maritime operations. The team includes domain experts in physics-informed machine learning, simulation acceleration, predictive maintenance, computer vision, and operational analytics. This internship is part of a strategic project aimed at accelerating complex simulations for ship performance using machine learning and graph-based AI.
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 in predicting how a vessel behaves in water, but they can take hours to compute. Instead of running time-consuming physics-based simulations, we use geometric deep learning , a type of machine learning that can learn from vessel designs and quickly estimate results like 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/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 in our team together with Data Scientists , domain knowledge naval architects, and external partners such as MARIN.
