Amsterdam
20 May 2026
Master Thesis: Physics-Informed Machine Learning with Applications in Hydrogen Fuel Propulsion Systems
NLR - Netherlands Aerospace Centre
Amsterdam
20 May 2026
Master Thesis: Physics-Informed Machine Learning with Applications in Hydrogen Fuel Propulsion Systems
Master thesis on physics-informed machine learning for hydrogen fuel propulsion systems at NLR. Involves literature study, framework design, and validation. Requires MSc student in aerospace engineering, physics, or computer science with Python and ML experience.
AIOn-siteInternshipEntry LevelPythonMachine Learning
Master Thesis: Physics-Informed Machine Learning with Applications in Hydrogen Fuel Propulsion Systems
Master thesis on physics-informed machine learning for hydrogen fuel propulsion systems at NLR. Involves literature study, framework design, and validation. Requires MSc student in aerospace engineering, physics, or computer science with Python and ML experience.
AIOn-siteInternshipEntry LevelPython
Salary
Not specified
Core Qualifications
Technical (Must-have)
PythonMachine LearningPyTorchPhysics-Informed Machine Learning
Key Responsibilities
- Preliminary assessment and identification of suitable sub-systems and components for PIML-based modelling.
- Literature study on PIML and maintenance of hydrogen fuel propulsion systems.
- Designing and implementing a PIML framework to model key phenomena.
- Validating framework on real-world or simulation datasets and benchmarking against state-of-the-art methods.
Master ThesisPhysics-Informed Machine LearningHydrogen Fuel PropulsionPrognostics and Health ManagementPythonAerospaceNLRInternship