Post-doc position in seismology: new methodologies in data analysis

Offer description:

We are seeking a candidate for a project of data exploration in seismology. Large amounts of seismological data are available and the various tools developed with the rise of machine learning open new possibilities to track down signatures of physical processes at work in the depths of the Earth.

The classical data product in seismology is the event catalog. Considerable progress has recently been made in detection, both by implementing weak event detection through network response (Beaucé et al., 2022) and by developing statistical detectors robust to noise (El Bouch et al., 2022). In addition, an advanced data representation (ScatNet: Seydoux et al., 2020) gives access to new continuous characteristics of the signals that can be related to changes in the environment (e.g. Steinmann, 2022). These approaches will be applied to seismic and geodetic data sets.

The work is part of the activities of MIAI (Multidisciplinary Institute in Artificial Intelligence of Université Grenoble Alpes) and of ERC AdG F-IMAGE. The funding is for an initial one-year contract, renewable for a second year. The salary will be fixed according to the candidate’s research experience.

Qualifications:

• PhD in geophysics or signal processing.
• The work requires expertise in seismology, signal processing, data mining and Python programming skills.
• Years of Research Experience: 1-4

References:

• Beaucé, E., van der Hilst, R. D., & Campillo, M. (2022) Microseismic constraints on the mechanical state of the North Anatolian Fault Zone 13 years after the 1999 M7.4 Izmit earthquake. Journal of Geophysical Research: Solid Earth, 127, e2022JB024416.
• El Bouch, S., O. Michel, & P. Comon (2022) A normality test for multivariate dependent samples. Signal Processing Volume 201, December 2022, 108705.
• Seydoux, L., R. Balestriero, P. Poli, M. de Hoop, M. Campillo, R. Baraniuk (2020) Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning. Nature communications 11 (1), 1-12.
• Steinmann, R., Seydoux, L., & Campillo, M. (2022) AI-based unmixing of medium and source signatures from seismograms: ground freezing patterns Geophysical Research Letters 49, e2022GL098854.

Offer Starting Date: the position is to be filled as soon as possible.

To apply, please send your CV, a cover letter, a sample of recent publications and two references to: michel.campillo@univ-grenoble-alpes.fr (cc: marian.ramirez-nino@univ-grenoble-alpes.fr).