Postdoc H/F - Biomass (24 months)

Kayrros1; Paris; Sur place; EUR 35 000 - 55 000; Plein temps

Résumé du poste

An innovative firm is seeking a motivated postdoc to contribute to groundbreaking research on forest biomass quantification using high-resolution satellite imagery and artificial intelligence. This role involves developing advanced methods for forest mapping, processing satellite data, and collaborating with leading researchers in the field. The ideal candidate will have a strong background in remote sensing and deep learning, with a passion for environmental science. Join a dynamic team at the forefront of climate research, where your contributions will help shape sustainable forest management practices worldwide.

Qualifications

• PhD or equivalent in a relevant field with experience in remote sensing and deep learning.
• Strong background in environmental science and data analysis.

Responsabilités

• Develop methods to map forests using satellite imagery and AI.
• Process and analyze data from various sources for forest monitoring.
• Publish research findings in peer-reviewed journals.

Connaissances

Deep Learning

Remote Sensing

Satellite Imagery Analysis

Data Processing

Machine Learning

Statistical Modeling

Formation

PhD in Environmental Science

Master's in Computer Science

Outils

Sentinel 1 and 2

LiDAR

Gedi

SPOT 6-7

Alsar

Nisar

Icesat 2

Pleiades

Description du poste

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

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.

The missions will cover data processing, model design, training, inference, interpretation of results and publication in peer-reviewed journals:

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.

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).

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).

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).

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.

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.

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.

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.

Supervise students, engineers and work with colleagues of the research group for joint publications.

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