Job offer

Aix-Marseille Université; France; Sur place; EUR 35 000 - 55 000; Plein temps

Résumé du poste

Une entreprise innovante recherche un chercheur postdoctoral pour travailler sur la modélisation des inondations en utilisant des données géographiques et des techniques de traitement d'images. Le candidat idéal aura un doctorat récent et des compétences solides en Python. Ce poste offre l'opportunité de contribuer à des recherches cruciales sur la vulnérabilité des populations face aux inondations, en intégrant des données variées dans un système d'information géographique. Rejoignez une équipe dynamique à Aix-Marseille Université et participez à des projets de recherche passionnants qui auront un impact significatif sur la gestion des risques d'inondation.

Qualifications

• Ph.D. en Télédétection/Geomatique obtenu dans les 3 dernières années.
• Compétences en Python et traitement d'images requises.

Responsabilités

• Collecte de données géographiques multi-sources pour modélisation hydraulique.
• Développement d'algorithmes de traitement d'images pour cartographie.

Connaissances

Python pour les données géographiques

Traitement d'images/signaux

Modèles hydrauliques

Formation

PhD ou équivalent

Outils

Système d'information géographique (SIG)

Radar à synthèse d'ouverture (SAR)

Description du poste

Organisation/Company: Aix-Marseille Université

Department: IUSTI

Research Field: Geography » Cartography, Computer science » Modelling tools, Environmental science » Earth science

Researcher Profile: First Stage Researcher (R1)

Positions: Postdoc Positions

Country: France

Application Deadline: 15 May 2025 - 12:00 (Europe/Paris)

Type of Contract: Temporary

Job Status: Full-time

Offer Starting Date: 4 Apr 2025

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

Context
The vulnerability of populations to flooding is exacerbated worldwide due to the combined effects of climate change and socio-economic factors (Lehner et al., 2006, Gosset et al., 2023). This vulnerability is more pronounced in the Global South due to a lack of protective measures, monitoring data and forecasting tools to anticipate extreme events. Cambodia, for example, is regularly affected by significant floods from the Mekong and Tonlé Sap rivers. These floods have both beneficial effects on agriculture by providing water and enriching soils, and detrimental effects when they are exceptional and cause large-scale, long-lasting floods that pose challenges to human, health and food security. In this context, it is crucial to predict and characterize flood risk. Shallow Water 2D numerical models are well-suited for modeling free-surface flows. However, their application to risk management is still hindered by uncertainties, particularly due to the lack of knowledge about the topography of the study area. The floodplains in Cambodia are a prime example. The topography is significantly marked by drainage networks (Preks) set up for agriculture, which are poorly mapped and whose geometries are largely unknown, despite their important role in water propagation during rising and falling water levels.

In urban environments, land use maps provide information about buildings, roads and vegetated areas, but "hydraulically relevant" elements that can divert or store water, such as small walls, ditches, underground parking lots or retention basins, are generally not mapped.

Assigned Mission
The objective is to collect multi-source, multi-format data and integrate it into a geographic information system (GIS) suitable for incorporating relevant information into hydraulic models. The format of this GIS will need to be determined; indeed, while topographic information is typically represented in a raster-based digital elevation model, it would be beneficial to retain vector formats where possible, due to their lighter data management requirements. The mission will initially involve collecting existing geographic data in "shapefile" format for the modeled areas, whether urban or along the Mekong, and extracting useful information for the hydraulic model. Given the relatively limited and inadequate information sources, the next objective is to use remote sensing data from Earth observation. Synthetic Aperture Radar (SAR) satellite imagery is best suited to provide spatialized, large-scale information on flooded areas day and night, regardless of cloud cover. Optical images also provide relevant observations for better mapping of land use and numerous objects of interest. The second part of the mission will involve developing and implementing image processing algorithms to produce land use maps and identify drains and hydraulic structures (dikes, etc.) that are not represented in digital elevation models but are necessary for setting up a hydraulic model. The missions and objectives may be adapted based on the skills of the recruited individual.

Where to apply

E-mail: [e-mail]

Requirements

Research Field: Geography » Cartography

Education Level: PhD or equivalent

Skills/Qualifications

• Proficiency in Python for geographic data.
• Image/signal processing
• Knowledge of hydraulic models would be a plus

Ideally, the recruited individual will have a Ph.D. in Remote Sensing/Geomatics (defended within 3 years max), with proficiency in the Python programming language.

Specific Requirements

PhD obtained less than 3 years ago

Languages

ENGLISH Level: Good

FRENCH Level: Basic

Additional Information

Selection process
Send motivation mail with “SWIFTS Postdoc” as subject to [e-mail] along with an attached CV, before 15 May 2025.

Additional comments
The position is funded for 1 year, under the ANR project (SWIFTS), and the recruited individual will be required to report both in writing and orally during project meetings. The work will be carried out at the IUSTI laboratory of Aix Marseille University (AMU) in collaboration with IRD (UMR EspaceDev, Montpellier) and CERFACS and CNES in Toulouse. Depending on the profile of the recruited individual, a second year of funding may be offered on another project.