PhD Position in Machine Learning Seismology
PhD Position in Machine Learning Seismology
100%, Zurich, fixed-term
The Swiss Seismological Service (SED) at the Department of Earth Sciences at ETH Zürich invites applications for a fully funded 4-year PhD position in Machine Learning Seismology and Induced Earthquakes. The preferred starting date for this position is June - September 2024. This PhD position is supported by a Swiss National Science Foundation (SNSF) funded project EFFSIMMSI, led by Dr. Peidong Shi and collaborators at ETH Zurich and INGV.
Project background
Within the framework of the SNSF-funded project “EFFSIMMSI: Advancing Induced Earthquake Forecasting and Fracturing Dynamics via Innovative Scale-Invariant Seismic Monitoring and Multi-Sensor Integration”, we aim to further develop novel seismic monitoring and analysis methods utilizing machine learning techniques to increase our understanding of induced earthquakes. This project addresses the central theme of investigating rupture dynamics of induced earthquakes and advancing our capacity to forecast them more reliably. We will employ the developed methodologies to analyze data collected from various scales and geological conditions, including laboratory rock physics experiments, underground fluid injection experiments, and enhanced geothermal systems worldwide.
Job description
The PhD student will focus on constructing and training advanced machine learning models tailored to characterize induced earthquakes recorded by various instruments, including distributed acoustic sensing, acoustic emission sensors, and geophones. With these efforts, the PhD candidate will improve the current state-of-the-art of real-time seismic monitoring and induced earthquake forecasting by implementing advanced machine-learning techniques and integrating physical understandings of rupture dynamics. The PhD candidate will apply the developed methodologies and models to various geological test sites to extract high-resolution earthquake catalogs, analyze rock rupture mechanisms, and benchmark different induced earthquake forecasting models.
Your profile
We are seeking a highly motivated candidate with a strong interest in machine learning, seismic monitoring and earthquake seismology. The ideal candidate should:
- A Master’s degree in Earth Sciences / Physics / Mathematics / Computer Sciences or a related discipline is required. Applicants must have obtained their Master's degree by September 2024
- A strong foundation in analyzing large datasets and machine learning is highly desirable for this position
- Proficiency in modern scientific programming languages (e.g., advanced Python, C, CUDA etc) and parallel computing would be advantageous
- Strong background and experience in computational methods and earthquake monitoring would be an asset
- The PhD student will be required to work as part of an international team. We presuppose abilities in coherent scientific teamwork, excellent communication skills (spoken and written English) and the capability of a good work organization as far as precise way of working
Your workplace
Your workplace
We offer
The work will be conducted at SED under the supervision of Dr. Peidong Shi and Prof. Stefan Wiemer. The student will also benefit from interdisciplinary and interinstitutional collaboration with international experts in machine learning, seismic monitoring/imaging, statistical seismology, and geomechanical modelling. Project team members include Dr. Federica Lanza (ETH Zürich), Dr. Luigi Passarelli (INGV), and Dr. Antonio Pio Rinaldi (ETH Zürich). In specific, we will provide:
- An exciting working environment
- Perspectives for career development
- Opportunities to learn cutting edge techniques
- A diverse and interdisciplinary team
ETH Zurich is a family-friendly employer with excellent working conditions. You can look forward to an exciting working environment, cultural diversity and attractive offers and benefits.
We value diversity
Curious? So are we.
We look forward to receiving your online application by June 15th, 2024, with the following documents:
- Motivation Letter (no more than 2 pages)
- describe your research achievements and research interests
- demonstrate your interest and suitability for the offered position
- Full CV
- Undergraduate and graduate transcripts
- Contact details of two referees
Please note that we exclusively accept applications submitted through our online application portal (do not send any applications by e-mail). The review process is scheduled to commence on May 24th, 2024, and will continue until the position has been filled. Applications received before May 24th will be given full consideration. Therefore, we encourage applicants to submit their applications as early as possible. Applications via email or postal services will not be considered.
Further information about the Swiss Seismological Service can be found on our website . Questions regarding the position should be directed to Dr. Peidong Shi, email: Write an email (no applications).
About ETH Zürich
Curious? So are we.
We look forward to receiving your online application by June 15th, 2024, with the following documents:
- Motivation Letter (no more than 2 pages)
- describe your research achievements and research interests
- demonstrate your interest and suitability for the offered position
- Full CV
- Undergraduate and graduate transcripts
- Contact details of two referees
Please note that we exclusively accept applications submitted through our online application portal (do not send any applications by e-mail). The review process is scheduled to commence on May 24th, 2024, and will continue until the position has been filled. Applications received before May 24th will be given full consideration. Therefore, we encourage applicants to submit their applications as early as possible. Applications via email or postal services will not be considered.
Further information about the Swiss Seismological Service can be found on our website . Questions regarding the position should be directed to Dr. Peidong Shi, email: Write an email (no applications).
