AI-Enhanced Physics-Based Simulations for Earthquake Modelling

The Computational Geophysics Lab at the Asian School of the Environment, NTU Singapore, is offering a fully funded PhD position in collaboration with the University of Cambridge. The project sits at the intersection of computational geophysics and artificial intelligence and aims to advance our understanding of earthquake physics by integrating large-scale simulations of dynamic rupture and long-term seismic cycles with cutting-edge AI techniques. While deterministic earthquake prediction is not possible, physics-based simulations are beginning to replicate the complex, nonlinear behaviour of real fault systems. This project will build on those tools to investigate how AI methods, such as machine learning, neural operators, and surrogate modelling, can be used to detect patterns in earthquake simulations, accelerate computational workflows, and connect simulations with observational data such as GPS and seismicity records.

The PhD student will be supervised by Asst. Prof. Luca Dal Zilio (NTU, Earth Observatory of Singapore) and will benefit from joint supervision, participation in international workshops, and a dynamic academic setting.

 The successful candidate will:

• Design AI-driven tools (e.g., surrogate models, neural operators) to support large-scale earthquake simulations
• Analyze complex rupture dynamics and identify potential precursory features
• Integrate geophysical data (e.g., GPS, seismicity) to improve model realism
• Collaborate within an international research team across NTU Singapore and the University of Cambridge

We are looking for candidates who:

• Hold a bachelor’s or master’s degree in geophysics, physics, mathematics, engineering, or computer science.
• Have a strong background in physics and mathematics, and familiarity with AI or machine learning.
• Are proficient in scientific programming (e.g., Python, Julia).
• Are curious, self-driven, and motivated to work across disciplines.

To apply, please send the following documents to [email protected]

• A one-page motivation letter.
• Your full CV.
• Academic transcripts.
• Contact information of two academic referees.