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data science machine learning python jobs near kanton zürich

Doctoral student in induced and natural seismicity forecasting

ETH Zürich -
Zürich, ZH

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100%

Qualifikationen

Oracle
Englisch
Master
Analysefähigkeit
Python

Vollständige Stellenbeschreibung

Doctoral student in induced and natural seismicity forecasting

100%, Zurich, fixed-term

The 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.

Job description

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

Profile

Education: An MSc degree in Geophysics, Physics, Mathematics, Data Science, or a related quantitative field
Technical Skills: Strong programming proficiency (primarily Python)
Domain Interest: A deep interest in seismology, induced seismicity, and the application of statistics and machine learning to earth science problems
Analytical Skills: High-level quantitative skills and a structured approach to complex problem-solving
Communication: Ability to work effectively in a collaborative environment with full proficiency in English (written and oral)

Workplace

We offer

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). This is a 100% position with a competitive salary in line with standard ETH doctoral rates, based in the heart of Zurich.

We value diversity and sustainability

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future.

Curious? So are we.

We look forward to receiving your online application with the following documents:

Motivation letter
CV
Academic transcripts
Contact details of 2–3 references

Further information about the Swiss Seismological Service at ETH Zurich can be found on our website. Questions regarding the position should be directed to Dr. Leila Mizrahi and Dr. Vanille Ritz ([email protected], [email protected]) (no applications).

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence.

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Doctoral student in induced and natural seismicity forecasting

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