Post-doc on the estimation of dead fuel related to forest decline in metropolitan France M/F

CNRS; France; Sur place; EUR 35 000 - 45 000; Plein temps

Résumé du poste

A prominent research organization in France is offering a postdoctoral position focused on assessing dead fuel produced by forest dieback. This project combines satellite-based remote sensing and modeling to analyze forest fire risks, especially due to climate change. Responsibilities include conducting literature reviews, improving detection processing chains, and collaborating with other researchers. Ideal for candidates with strong Python skills and interest in transdisciplinary research. This role is a full-time, temporary post starting March 2026.

Qualifications

• Proven experience in Python programming to automate processing.
• Familiarity with optical, radar and lidar imagery required.
• Interest in teamwork and collaboration is essential.

Responsabilités

• Conduct literature review on forest dieback and fuels.
• Improve satellite-based detection processing chain.
• Determine dead fuel related to dieback using satellite data.
• Engage in transdisciplinary collaboration with another postdoc.
• Write scientific article(s).

Connaissances

Python programming

Handling optical, radar, and lidar imagery

Machine/Deep learning and classification

Knowledge of the Linux operating system

Interest in transdisciplinarity and teamwork

Autonomy and scientific rigor

Description du poste

Organisation/Company CNRS Department Centre d'études spatiales de la biosphère Research Field Environmental science Biological sciences Geosciences Researcher Profile Recognised Researcher (R2) Country France Application Deadline 31 Dec 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 2 Mar 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Winner of the CNRS AMI PISE call, the ECOFEU project – in collaboration with CNRM (Météo-France/CNRS) and CECI (Cerfacs/CNRS) – addresses the growing risk of forest fires in France, exacerbated by climate change and forest dieback. By combining space-based remote sensing and modeling, it aims to better understand the evolution of forest fuels and their role in fire propagation. Tested on pilot forest areas (Centre-Val de Loire and Pyrénées-Orientales) in connection with field stakeholders, this approach will help improve prevention, strengthen forest resilience, and protect populations, ecosystems, and the forestry economy.

This postdoctoral project focuses on estimating dead fuel produced by forest dieback. In previous projects, a processing chain using Sentinel-2 data has already been developed (Mouret et al., 2024). The objective here is to improve this chain and derive dead fuel estimates, which will be integrated into forest fire spread simulations conducted by another postdoc at the Météopole. Both postdocs will collaborate closely. Since the presence of understory vegetation is also critical for fire propagation, a prospective study on detecting the lower vegetation layer through remote sensing will also be carried out on this topic.

Responsibilities

• Conducting a literature review on the link between forest dieback and fuels (dead and live)
• Using and improving the satellite-based forest dieback detection processing chain (RECONFORT)
• Determining dead fuel related to dieback using satellite data, first in the Centre-Val de Loire region, then across France
• Carrying out prospective work on detecting the understory vegetation layer through remote sensing
• Engaging in transdisciplinary collaboration and exchanges with another postdoc within the project
• Writing scientific article(s)

The Centre d'Études Spatiales de la BIOsphère (CESBIO) is a joint research unit (CNES, CNRS, IRD, INRAE, University of Toulouse) of around 130 people, based mainly in Toulouse. Its mission is to improve the understanding of the functioning of the continental biosphere and its interactions with climate and human activities, relying on a threefold approach: spaceborne remote sensing, modeling, and experimentation. The laboratory contributes to the specification and analysis of data from major space missions (SMOS, Biomass, Venµs, Trishna) and develops models capable of describing and predicting the evolution of continental surfaces under various pressures. CESBIO is organized into two research teams (modeling and observation systems) and maintains a strong international outlook through collaborations and missions abroad.

Technical Skills

• Python programming to automate processing chain
• Handling optical, radar, and lidar imagery
• Machine/Deep learning and classification
• Knowledge of the Linux operating system for using a computing cluster
• Interest in transdisciplinarity and teamwork
• Autonomy and scientific rigor