Internship: Fast Physics
Core
Accelerate high-fidelity computational fluid dynamics (CFD) simulations of ship hulls using geometric deep learning and graph neural networks to predict water resistance and flow fields.
Role type
Intern, Machine Learning Engineer (Physics-Informed)
Builds
A prototype system for early-stage design exploration and simulation optimization in shipbuilding.
Domain
Maritime industry, Computational Fluid Dynamics, Physics-Informed Machine Learning
Deliverable
production ML models
Required skills
Python, Geometric Deep Learning, Graph Neural Networks, CFD simulation data, 3D geometry formats
Preferred skills
PyTorch, TensorFlow, Naval architecture knowledge
Technologies
PyTorch, TensorFlow, Python
Responsibilities
Support improvement of ML-based frameworks, preprocess CFD simulation data and ship hull geometries, run experiments in Python, document results and present findings
Seniority
Intern, Academic level