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

Organisation/Company: Université Grenoble Alpes
Department: Institute of Earths Science
Research Field: Environmental science, Earth science, Geosciences
Researcher Profile: Recognised Researcher (R2)
Country: France
Application Deadline: 28/02/2023 - 23:59 (Europe/Paris)
Type of Contract: Temporary
Job Status: Full-time
Hours Per Week: 35
Offer Starting Date: 01/03/2023 - 12:00

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 funding is for an initial one-year contract, renewable for a second year.

The work is part of the activities of MIAI (Multidisciplinary Institute in Artificial Intelligence of Université Grenoble Alpes) and of ERC AdG F-IMAGE.

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.

Requirements

Research Field: Geosciences
Education Level: PhD or equivalent

Skills/Qualifications
The work requires expertise in seismology, signal processing, data mining and Python programming skills.

Specific Requirements
PhD in geophysics or signal processing
Languages: ENGLISH
Level: Excellent

Research Field
Environmental science > Earth science
Years of Research Experience: 1 - 4

Additional Information

Selection process
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)

Applications will be accepted until the position will be filled.

Website for additional job details:
F-image
MIAI