Internship: Fast Physics

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 AI research project aimed at accelerating complex ship-performance simulations using physics-informed machine learning, neural operators, geometric deep learning, and emerging Physics Transformer models. 

As an intern, you will work on the Fast Physics project, where the main objective is to research, improve, and extend AI models that can act as fast surrogate models for high-fidelity ship-performance simulations. The internship is primarily focused on artificial intelligence and scientific machine learning, with CFD data used as the learning target and validation basis. 

Rather than running time-consuming physics-based simulations for every design iteration, we develop AI architectures that learn from vessel geometries, operating conditions, and simulation outputs to estimate quantities such as resistance, pressure distributions, and flow fields. A central topic is the exploration of recent Physics Transformer models and neural-operator architectures, and how these can be adapted to complex maritime geometries. 

You will contribute to improving our current Physics Transformer model and architecture by benchmarking recent research, designing model improvements, running training experiments, and validating performance across different hull forms, operating conditions, and simulation fidelities. The outcome should be a stronger AI model prototype and a clear research contribution on how transformer-based physics models can support fast simulation and early-stage design exploration. The assignment can be a thesis/graduate internship and could start from September onwards. 

• Design and run AI experiments in Python using PyTorch, including model training, validation, benchmarking, and ablation studies. 

• Work with CFD simulation data, ship hull geometries, and numerical outputs as input for model development and evaluation. 

• Collaborate with Data Scientists, naval architects, and external research partners to translate technical requirements into AI model improvements. 

• Document results, compare model variants, and present findings and recommendations to the team regularly. 

• Has strong programming experience in Python and hands-on experience with deep learning frameworks such as PyTorch; experience with transformer architectures, graph neural networks, neural operators, or scientific machine learning is highly preferred. 

• Has an affinity with physics-informed AI, surrogate modeling, or simulation acceleration; familiarity with CFD data, 3D geometry, meshes, or numerical simulation outputs is considered a plus. 

• Is motivated to research and improve state-of-the-art AI model architectures, especially Physics Transformer models, for real-world engineering applications. 

• Communicates fluently in English.