Data Scientist - PostDoc (France) at EarthDaily Agro

Balma, Occitania, France -

Full Time

Start Date

Immediate

Expiry Date

26 Apr, 26

Salary

0.0

Posted On

26 Jan, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Geospatial Science, Remote Sensing, Data Science, Computer Science, Python, GDAL, Rasterio, GeoPandas, xarray, Machine Learning, TensorFlow, PyTorch, Scikit-learn, Cloud Computing, Big Data Processing, Communication Skills

Industry

Software Development

Description

ABOUT EARTHDAILY EarthDaily is revolutionizing the way we understand and monitor our planet. Through cutting-edge Earth Observation (EO) technology and geospatial analytics, we provide unparalleled insights for industries ranging from agriculture to mining, insurance, and government intelligence. Our mission is to build the world’s most advanced change detection system to capture, analyze, and interpret global shifts in near real-time. THE ROLE EarthDaily is looking for a PostDoc in remote sensing to join its Data Science team and develop innovative methods for processing multi-scale observations. You will be involved in the development of methods in the framework of the PHENET European project WP3 (https://www.phenet.eu/en). You will develop a multi-scale image harmonization framework for the analysis of automatically co-registered multi-resolution datasets (Sentinel-2, UAV, EarthDaily, on-field sensors). The spectral and radiometric adjustment algorithms should be optimized to scale up surface reflectance from different sensors using physical and/or AI methods. These data fusion algorithms will be provided to enable the synergistic exploitation of IoT and EA time series based on Bayesian multi-sensors fusion and interpolation methods. In PHENET, the European Research Infrastructures (RI) on plant phenotyping (EMPHASIS), ecosystems experimentation (AnaEE), long-term observation (eLTER) and data management and bioinformatics (ELIXIR) join their forces to co-develop new tools and methods for the identification of future-proofed combinations of species, genotypes and management practices in front of the most likely climatic scenarios across Europe. YOUR MISSIONS Conduct a state-of-the-art review of data fusion methods applied to satellite/UAV/IoT data Develop and test fusion methodologies considering pro and cons of each type of observations (spatial and temporal resolution, geolocation precision, spectral information, radiometric quality…) Leverage cloud infrastructure and data access Conduct an objective comparative study between identified approaches Analyze performance and present results Collaborate with our Phenet academic partners (UCL, INRAe) Interact daily with an international, multidisciplinary team EDUCATION, KNOWLEDGE AND SKILLS PhD in Geospatial Science, Remote Sensing, Data Science, Computer Science, or a related field. Autonomous and creative Strong proficiency in Python and geospatial libraries such as GDAL, Rasterio, GeoPandas, and xarray Experience with remote sensing techniques and platforms / sensors (e.g., Sentinel, Landsat) Knowledge of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) and statistical modeling Familiarity with cloud computing environments (AWS, Google Cloud, or Azure) and big data processing Strong communication skills and ability to work in an international, cross-functional team. Fluent in English (oral and written): meetings with partners and internal are mostly in English. Additionnal "good to have" Background in agriculture, agronomy, or environmental science. Knowledge of deep learning techniques for geospatial applications. Familiarity with Agile development methodologies. Proficiency in database management for geospatial datasets. CONDITIONS Full-time job from May 2026 (12-15 months) Location: Based in Balma (Toulouse, France). Two days of work from home possible. Mutuelle and lunch vouchers.

Responsibilities

The role involves developing innovative methods for processing multi-scale observations and conducting a state-of-the-art review of data fusion methods. Additionally, the candidate will collaborate with academic partners and analyze performance results.