Date added: 2026-04-09
Summer School in RWTH Aachen: Physics-Informed Machine Learning for Geotechnical Engineering
Physics-Informed Machine Learning for Geotechnical Engineering
ENHANCE Erasmus+ Blended Intensive Programme | Summer School 2026
❗️Application deadline: April 30, 2026
About the programme
The aim of the programme is to provide participants with an understanding of the principles of physics-informed machine learning and to demonstrate how these methods can be applied to real-world engineering problems. The course consists of two parts: a one-week online module and intensive on-site workshops hosted at the RWTH Aachen campus.
The programme combines machine learning, physical modelling, and geotechnical engineering, enabling students to work in international and interdisciplinary teams.
Course structure
1. Online phase: July 13–17, 2026
During the online week, participants are introduced to the fundamentals of machine learning and its applications in engineering. Topics include, among others:
- data preparation and processing,
- optimization techniques,
- basic machine learning models,
- physics-informed neural networks (PINNs),
- connections between models, differential equations, and gradients.
Participants work using Python and scientific computing tools, including Jupyter Notebooks.
2. On-site workshop: July 20–24, 2026, RWTH Aachen
The second part of the programme takes the form of hands-on, hackathon-style workshops. Working in teams, students solve real-life modelling problems by applying the knowledge gained during the theoretical phase.
The workshops provide deeper insight into the capabilities and limitations of physics-informed machine learning methods and offer an excellent opportunity to network with students and academic staff from across Europe.
Who is it for?
The programme is aimed at Master’s level students from ENHANCE partner universities, studying fields related to:
- engineering,
- geophysics,
- computer science,
- applied mathematics.
What do participants say?
“What happens when a computer scientist dives into predicting underground water flow? It turns out that both water and ideas flow better with the right team. Taking part in this summer school taught me not only about neural networks, but also how much you can gain by stepping outside your comfort zone and working across disciplines.”
Jakub Sochacki, participant of the 2025 edition
Why should you apply?
- international academic environment,
- practical, hands-on approach to machine learning,
- combining theory with real-world engineering problems,
- short-term study experience in Germany,
- funding available through the Erasmus+ programme.
Applications
No summer plans yet?
Apply for the Physics-Informed Machine Learning for Geotechnical Engineering Summer School at RWTH Aachen!
❗️Application deadline: April 30, 2026
Application form:
Apply here!
Tags: #GeotechnicalEngineering #PhysicsInformed #MachineLearning #RWTHAachen #Germany #SummerSchool #ENHANCEAlliance
Photos: Jakub Sochacki, RWTH Aachen University