Doctoral student in induced and natural seismicity forecasting

The doctoral student will develop, integrate, and evaluate forecasting models of induced and natural earthquakes. The responsibilities include: Model Integration: Incorporating state-of-the-art models, specifically ETAS-f and Oracle, into the SED's forecast management and scheduling system; Systematic Evaluation: Performing rigorous model comparison tests to determine forecast quality; Surrogate Modeling: Building, training, and evaluating machine learning surrogate models to emulate complex seismic behaviors and accelerate forecasting; Data Engineering: Populating and managing data lakes with large-scale natural ETAS simulations and synthetic datasets to provide the foundation for AI training; Scientific Communication: Publishing findings in high-impact journals and presenting at international conferences.

The Swiss Seismological Service operates at the unique intersection of academic research and a federal public mandate, fostering an environment where diverse perspectives are essential for addressing complex geohazard challenges. The candidate will be part of a community that values mutual mentorship and collective growth, supported by a world-class academic environment: ETH Zurich is currently ranked #1 globally for Earth Sciences (QS World University Rankings 2026).

• An MSc degree in Geophysics, Physics, Mathematics, Data Science, or a related quantitative field

• Strong programming proficiency (primarily Python)

• A deep interest in seismology, induced seismicity, and the application of statistics and machine learning to earth science problems

• High-level quantitative skills and a structured approach to complex problem-solving

• Ability to work effectively in a collaborative environment with full proficiency in English (written and oral)