Postdoctoral researcher for dynamic vegetation modeling and satellite-based fire analysis- NL

In collaboration with Utrecht University, you will quantify fire dynamics and its impacts on vegetation and climate at regional scales, combining global vegetation models with state-of-the-art remote sensing data.

Changing fire dynamics will be studied using a Dynamic Global Vegetation Model (DGVM), allowing us to quantify different mechanistic processes. Using high-resolution satellite-based estimates of trace gas emissions and albedo during and after wildfire events we will evaluate and improve the modeling of fire-related non-CO2 emissions and albedo changes. This will lead to better predictions of fire extents and its impacts on vegetation at regional scales.

This is a contribution to INFLAMES (), a national research project on fires, with partners from seven research institutes and universities in the Netherlands.

• Adapting the fire component of a Dynamic Global Vegetation model with improved representation of fire attributes of vegetation.
• Evaluating and improving modeled non-CO2 fire emissions during fires.
• Evaluating and improving modeled albedo changes after fires, including recovery and possible regrowth of vegetation.
• Making predictions of future fire dynamics and its impacts on vegetation at regional scales.

Leiden University is situated between Amsterdam (36 km) and The Hague (15 km) and was founded in 1575, making it the oldest university in the Netherlands. It houses around 35,000 students originating from 112 nationalities, facilitated by approximately 8,000 staff.

The Institute of Environmental Science (CML) at Leiden University offers a vibrant and supportive community of scientists committed to the challenges of sustainability. CML distinguishes itself for its attention to professional development in coordination with the interests of its scientists. As such, there are plenty of opportunities to learn new skills, expand your knowledge, collaborate across disciplines, and experiment in a friendly environment fully embodying academic freedom across all career levels.

The core focus of CML is to perform research and education in the interdisciplinary field of Environmental Sciences. CML focuses on the sustainable use of natural resources and preservation of natural capital and biodiversity. CML has a culture of mutual support and collaboration between researchers. CML has two Departments:
Industrial Ecology (CML-IE) and Environmental Biology (CML-EB). Presently, about 150 fte (including postdocs and PhDs) are employed  further collaborates with TU Delft and Erasmus University in the Leiden-Delft-Erasmus Centre for Sustainability (LDE-CfS), and with the Naturalis Biodiversity Centre.

The Institute of Environmental Sciences (CML) is positioned in the Faculty of Sciences. The Faculty of Science is a world-class faculty where staff and students work together in a dynamic international environment. It is a faculty where personal and academic development are top priorities. Our people are committed to expanding fundamental knowledge by curiosity and looking beyond the borders of their own discipline;

their aim is to benefit science and to make a contribution to addressing the major societal challenges of the future. The research carried out at the Faculty of Science is very diverse, ranging from mathematics, information science, astronomy, physics, chemistry and bio-pharmaceutical sciences to biology and environmental sciences. The research activities are organized in eight institutes. These institutes offer eight bachelor’s and twelve master’s programmes.

The faculty has grown strongly in recent years and now has more than 2.800 staff and almost 6000 students. We are located at the heart of Leiden’s Bio Science Park, one of Europe’s biggest science parks, where university and business life come together.

For more information, see (Use the "Apply for this Job" box below). Information about CML can be found versiteitleiden.nl/en/science/environmental-sciences.

A successful candidate has

• Experience in working with large geospatial datasets (e.g., ERA5, CMIP6, Sentinel, MODIS)
• Working with complex models and high…