Ph3Doctoral student in induced and natural seismicity forecasting /h3 pThe Swiss Seismological Service (SED) at the Department of Earth and Planetary Sciences (D-EAPS) is seeking a motivated doctoral student to work at the intersection of statistical seismology and machine learning. This position is part of two EU‑funded projects: GeoTwins, which focuses on creating digital twins for geothermal systems, and Earth‑AID, which aims to develop an AI‑powered decision‑support system for geohazards. The position will contribute to advancing earthquake forecasting and risk management and improve our ability to mitigate induced seismicity in real time. /p pThe doctoral student will develop, integrate, and evaluate forecasting models of induced and natural earthquakes. The responsibilities include: /p ul liModel Integration: Incorporating state‑of‑the‑art models, specifically ETAS‑f and Oracle, into the SED's forecast management and scheduling system. /li liSystematic Evaluation: Performing rigorous model comparison tests to determine forecast quality. /li liSurrogate Modeling: Building, training, and evaluating machine learning surrogate models to emulate complex seismic behaviors and accelerate forecasting. /li liData Engineering: Populating and managing data lakes with large‑scale natural ETAS simulations and synthetic datasets to provide the foundation for AI training. /li liScientific Communication: Publishing findings in high‑impact journals and presenting at international conferences. /li /ul h3Profile /h3 ul liEducation: An MSc degree in Geophysics, Physics, Mathematics, Data Science, or a related quantitative field. /li liTechnical Skills: Strong programming proficiency (primarily Python). /li liDomain Interest: A deep interest in seismology, induced seismicity, and the application of statistics and machine learning to earth science problems. /li liAnalytical Skills: High‑level quantitative skills and a structured approach to complex problem‑solving. /li liCommuni...