Data Scientist
Data Scientist
Max Planck Institute for Biogeochemistry
Jena, Germany
Background and position description
The Ecosystem Function from Earth Observation (EFEO) group of the Biogeochemical Integration department is opening a position for a remote sensing expert within the scope of the CoForFunc project, whose specific focus is on the Congo Basin Forest (CBF). Through a unique European research partnership and transnational collaborations with Central African country experts, the CoForFunc project aims at developing an integrated approach for the monitoring of tree functional diversity of the CBF to support biome-scale assessment of their vulnerability to expected climate change and human-induced transformations. The position is within a project work package dedicated towards providing a better understanding of Ecosystem Functional Properties (EFPs) from space in a challenging environment. The objective is to explore innovative uses of various types of coarse resolution satellite data to both circumvent instrumental artifacts of medium and high spatial resolution sensors and develop novel ways to map biome-scale forest functional properties.
Your tasks
- Design and apply a methodology to characterize tropical forest phenology over the CBF from hourly geostationary satellite time series of optical vegetation indices and compare with both ground/proximal data and high spatial resolution imagery
- Explore the possibility to upscale water status and structural traits to satellite RS in microwave frequencies over selected areas in the CBF
- Explore the possibility to extract photosynthetic traits over the CBF from sun-induced chlorophyll fluorescence retrieved from space
- Lead and contribute to scientific publications on the subject
- Assume responsibilities on project reporting and deliverables
Your profile
- Strong background in Environmental/Earth system science, ecology, environmental engineering, geography or a related field, ideally with a PhD in such field
- Strong experience in satellite remote sensing, particularly for variables related to plant physiology, functional diversity, ecology and ecosystem-level plant traits
- Strong programming skill in a modern language for data analysis (Python, Julia, R)
- Ability to work both independently and as part of a team
- Good knowledge of written and spoken English, and good communication
- Experience in linking the ground observations to coarse satellite pixels is an asset
- Strong experience in satellite-based sun-induced chlorophyll fluorescence (SIF) is an asset
- Experience with geostationary satellite imagery is an asset
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