Jobs in Toulon, France

Organisation/Company Université de Caen Normandie

Context
The postdoctoral position is part of the ANR ARHYCO project (ANR-24-CE55-4758-01), which aims to improve understanding of the role of roadside verges in the hydro-sedimentary (dis)connectivity of small agricultural watersheds. The aim of this project is to determine the extent to which this little-known feature of anthropized landscapes can be a marker of vulnerability but also a lever for integrated and rational management of runoff at the territorial level. Our premise is that an integrated and transdisciplinary approach is the most appropriate way to understand this feature in the dual context of ecological, climatic, and environmental transition and the transformation of rural landscapes. The project brings together researchers from various laboratories: UMR IDEES Caen and Rouen, UMR PRODIG Paris, UMR EVS Lyon, UMR Pacte Grenoble, UMR EDYSAN Amiens, UR ERPI Nancy, and UMR GREYC Caen.

Host structure
The IDEES joint research unit brings together professors, researchers, research and/or study engineers, and doctoral students. It has around 150 members working in fields that are recognized in France and internationally, such as spatial modeling and analysis, transport and port environments, health and risks, Information and Communication Technologies (ICT), and socio-territorial restructuring. Its identity is associated on the one hand with its multi-site organization (University of Caen Normandy, University of Le Havre Normandy, and University of Rouen Normandy) and its transdisciplinary project. Although the IDEES UMR is composed mainly of geographers, it also includes sociologists, historians, economists, and researchers in Information and Communication Sciences. More specifically, IDEES Caen conducts research on nature-society relations in a context of global and climate change. The research topics, which are carried out with various institutes (INEE, InSHS, INSERM, INSU, etc.), concern :

• the current dynamics of the land-sea continuum through high-frequency monitoring sites that are nationally certified (Dynalit and OMIV from INSU),
• natural hazards and risks (continental and marine hydrology, ground movement, soil and coastal erosion, hydrological and sedimentary transfers and connectivity, climatology/meteorology),
• health and technological risks (including epidemiology and the healthcare system),
• land use planning, social vulnerabilities, and economic issues (inequalities, accessibility, human development).

This research and its contribution to public policy are based on methods and techniques from geomatics, spatial analysis, multi-agent modeling and simulation, and laboratory work (soil and surface formation studies; host of the Solsup platform of the EMerode service unit).

General descriptions and main tasks of the postdoctoral position
You will conduct research in the interdisciplinary fields of remote sensing, spatial analysis, hydrology, and landscape. You will be expected to understand and analyze the spatial interactions between surface hydro-sedimentary flows and landscape structure.

You will work on task 1.1, which is dedicated to the development of methodological tools for the automatic characterization of roadside edges. Roadside objects can be small (grass strips) and/or three-dimensional (ditches, hedges) and extend over large areas (landscape scale). The post-doc will be expected to research the most suitable data for describing roadside areas, study their availability and/or acquisition methods, and then process this data. Preferred sites will be identified prior to the contract in line with the other objectives of the project and the availability of field data.

At the local level, a field inventory (including terrestrial laser scanning) and drone acquisitions (photogrammetry, multispectral imaging, and LiDAR) will be used to finely characterize road edges in relation to hydrosedimentary transfers. On smaller scales, aerial data (IGN LiDAR data and IGN BD Ortho IRC ortho-images) and high-resolution satellite images (SPOT, Pléiades, TerraSAR) will be used to map roadside areas. The field inventory will serve as a reference base for calibrating methodologies.

A reproducible processing chain adapted to the diversity of potentially mobilized data will need to be considered and developed in consultation with project partners, particularly those responsible for other project objectives. For example, digital image processing tools, such as filtering or mathematical morphology, could be evaluated to extract structural elements of road edges from images. By combining spectral and/or dimensional information, classification and/or deep learning methods may also be developed. In addition, the complementarity between the different data sources used (particularly between aerial LiDAR data and optical satellite images) will be evaluated in order to characterize objects of interest with the greatest possible accuracy. In order to improve the spatial resolution of images while maintaining broad spatial coverage, unmixing methods may be tested, using drone images as final elements to improve the spatial resolution of satellite images. Based on this remote sensing data, several indicators will be extracted to characterize roadside areas, including vegetation cover and structure, as well as slope and altitude parameters. Finally, automatic mapping of roadside areas will be proposed, in line with the other objectives of the project.

You will promote the results through technical reports and scientific articles, as well as by participating in conferences.

Work environment and context
The work will be carried out under the scientific supervision of Marianne Laslier (UMR EDYSAN 7058 CNRS), lecturer and researcher in ecology, geography, and remote sensing; Pauline Dusseux (UMR PACTE 5194 CNRS), lecturer-researcher in geography, environment, and remote sensing; Mohand Medjkane (UMR IDEES 6266 CNRS), research engineer in geography, geomatics, and databases; and Romain Reulier (UMR IDEES 6266 CNRS), project manager and professor-researcher in geography, hydrology, and hydro-sediment transfer.

The successful candidate will be based at the IDEES laboratory at the University of Caen Normandy and will have access to the necessary resources for data acquisition and processing to carry out their duties.

All mission and analysis costs will be financed from the ANR ARHYCO budget.

Required profile

• PhD in Geography (Remote Sensing, Geomatics), Computer Science, Agricultural Sciences
• Skills and/or knowledge in artificial intelligence (Machine Learning) and programming: proficiency in Python and/or R languages
• Proficiency in GIS software
• Strong skills in remote sensing data processing (Lidar, satellite, drone)
• Interest in agricultural and hydrological sciences
• Proficiency in written and spoken English
• Interest in and proficiency at working in a team in an interdisciplinary context
• Rigorous approach

Languages FRENCH Level Good

Languages ENGLISH Level Good

Selection process

Applicants are required to submit the following documents:

• A CV
• A cover letter (two pages) describing the research experience and the motivation for applying
• Publications/reports related to the postdoctoral position

Number of offers available 1

Company/Institute Université de Caen Normandie - UMR 6266 CNRS IDEES CAEN

Country France

City Caen

Postal Code 14000

Street Esplanade de la Paix