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Une opportunité de doctorat est proposée par un institut de recherche de premier plan en France, axée sur l'analyse de la micro-sismicité liée aux tremblements de terre. Le candidat idéal sera titulaire d'un Master en géophysique ou physique et possédera des compétences en mathématiques, physique, et programmation. Ce poste offre des avantages attractifs, y compris 45 jours de congés annuels et des options de télétravail.
Organisation/Company: CNRS
Research Field: Geosciences, Astronomy, Environmental Science
Researcher Profile: First Stage Researcher (R1)
Country: France
Application Deadline: 9 Jun 2025 - 23:59 (UTC)
Type of Contract: Temporary
Job Status: Full-time
Hours Per Week: 35
Offer Starting Date: 1 Oct 2025
Funding: Not funded through the EU Research Framework Programme
Research Infrastructure: No
The Centre national de la recherche scientifique (CNRS) is a leading global research institution exploring life, matter, the Universe, and human society. The thesis will be conducted within ISTerre, a joint research unit affiliated with Université Grenoble Alpes, CNRS, USMB, IRD, and Université Gustave Eiffel, located in Saint-Martin d'Hères and on the Bourget du Lac Science Campus. It focuses on studying the physics and chemistry of Earth, with activities including observation, experimentation, modeling, and hosting geophysical instruments.
The doctoral work will be in the "Seismic Cycle and Transient Deformations Team," comprising 42 members. The project is funded by an ANR consortium involving various geoscience and physics laboratories, with international collaborations, especially with Japanese researchers, to enhance data analysis and numerical simulations. Funding covers participation in conferences, workshops, and publication fees.
Benefits include 45 days of annual vacation, access to an inter-company restaurant, public transport reimbursement, mutual participation, telecommuting options, cultural and sporting activities, and sports facilities.
This PhD aims to analyze long-term microseismicity related to crustal earthquakes in Japan, focusing on the Kumamoto (2016) and Noto (2024) events, both preceded by foreshocks. The work involves using seismic catalogs, GNSS data for slow deformation detection, and applying Machine Learning techniques to improve event detection. The goal is to understand pre-seismic phases and the role of transient stresses in earthquake physics.
Interested candidates should submit a project-specific cover letter and CV.