Postdoc H/F - Biomass (24 months)
The Laboratoire des Sciences du Climat et de l’Environnement (LSCE) and Kayrros (a fast growing Paris based EO company) are looking for a motivated postdoc / young scientist candidate for a research project on ground-breaking methods to quantify forest biomass using very high resolution satellite imagery and artificial intelligence.
LSCE is a world-class research laboratory established and a collaboration between CEA, CNRS and the University of Versailles Saint-Quentin (UVSQ). It is part of the Institute Pierre Simon Laplace (IPSL). LSCE hosts approximately 300 researchers, engineers and administrative staff including many PhD and master’s students. LSCE will provide the employee with the opportunity to work directly on advanced methods with researchers from the LSCE and other institutions
Research scientist on forest monitoring using deep learning models and high resolution satellite images
Develop innovative methods to map forests at very high resolution using remote sensing data from multiple sources and deep learning models
Context:
The development of satellite imagery and LiDAR, combined with recent progress in AI, are disrupting the way forests are being monitored. While forests are in particular essential for carbon sequestration and biodiversity, they are profoundly affected by climate change and disturbances such as fire, droughts, deforestation events. Accurate and granular forest and forest change maps are essential for managers and public institutions in order to adapt management practices and policies.
The German French research project AI4Forest and the national project One Forest Vision have assembled a world leading international team of researchers in machine learning, remote sensing, forest ecology (Paris Laboratoire des Sciences du Climat et de l’Environnement LSCE and Ecole Normale Supérieure, INRAE, IRD, University of Münster, Technical University of Munich, Berlin University) to join forces for producing new accurate and periodically updated maps of forest attributes at global scale for forest structure (height), biomass carbon stocks, and activity data related to forest loss and gains (disturbances, including degradation, fires, clearcut) using cutting edge artificial intelligence models driven by satellite and field observations. The Paris team at Laboratoire des Sciences du Climat et de l’Environnement is collaborating with public institutions such as Office National des Forêts (ONF) and Institut Géographique National (IGN) to generate these maps over France and use them for the national inventory.
The LSCE is looking for an experienced and motivated researcher who will actively contribute to the production, validation, interpretation and publication of forest maps, with a focus on very high resolution (meter or submeter) and multi-modality.
Missions:
The missions will cover data processing, model design, training, inference, interpretation of results and publication in peer-reviewed journals:
Apply and improve existing deep-learning models of forest attributes.
Use innovative approaches to handle inputs at various resolutions (e.g., Sentinel and SPOT 6-7).
Scale canopy maps beyond Metropolitan France (e.g., other European countries, French Guiana).
Access and process satellite imagery and LiDAR using community tools developed by the lab (e.g., Sentinel 1 and 2, Gedi, LiDAR HD, SPOT 6-7).
Implement tools for inclusion of input data sources from multiple spaceborne and airborne platforms as input or validation of AI models (Alsar, Nisar, Icesat 2, orthophotos, Pleiades, airborne LiDAR) to improve the accuracy of monitoring of canopy height and biomass, and their changes over time.
Interpret the resulting maps of height and biomass changes using activity data such as degradation, deforestation features, and additional information on forest types, management, as well as climatic and soil drivers, by developing and using state of the art explainable machine learning and statistical models.
Promote and diffuse the results of research results at scientific conferences, and write research publications in collaboration with national and international experts. Several publications in high-profile journals are expected from the project.
Supervise students, engineers and work with colleagues of the research group for joint publications.
Profile:
You have a PhD in Remote Sensing / Forest monitoring / Machine Learning / Computer Science.
You have experience in building and using deep learning models, ideally in the context of EO-based monitoring of forests and / or land surface properties.
You have experience in remote sensing.
You have a publication track record and ability to present scientific results to the scientific community, as well as to a diverse range of stakeholders and public audience.
You like problem solving, you are autonomous, but want to work in a collaborative environment.
You are able to lead and structure research projects.
You are curious, enjoy learning and a research environment.
You want to work on problems that can benefit the environment.
Location: Laboratoire des Sciences du Climat et de l’Environnement (Saclay, in the Orme des Merisiers green area). Remote friendly. LSCE Is a world-class research laboratory established and a collaboration between CEA, CNRS and the University of Versailles Saint-Quentin (UVSQ). The LSCE hosts approximately 300 researchers, engineers and administrative staff including many PhD and master’s students. This project will provide the employee with the opportunity to work directly on advanced methods with researchers from the LSCE and other institutions.
Contract duration: 24 months, with an extension possible
Starting date: The position is open from Jan 20 2025 and will remain open until filled. The expected start of the position is April 2025.
Salary: Competitive salary, full social and health benefits, commensurate with work experience.
Supervision: Main supervisor: Philippe Ciais. Research director at LSCE
Co-supervisors: Ibrahim Fayad (LSCE), Martin Schwartz (LSCE)
How to apply: Applicants should submit a complete application package by email to the contacts below.
The application package should include (1) a curriculum vitae including e.g. important recent publications / projects, (2) statement of motivation (3) answers to the selection criteria above (4) names, addresses, phone numbers, and email addresses of at least two references.
Contacts:
philippe.ciais@lsce.ipsl.fr
ibrahim.fayad@lsce.ipsl.fr
martin.schwartz@lsce.ipsl.fr
References:
Schwartz, Martin, et al. "FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and Global Ecosystem Dynamics Investigation (GEDI) data with a deep learning approach." Earth System Science Data 15.11 (2023): 4927-4945.