Doctoral fellow

Organisation/Company Ghent University Research Field Agricultural sciences » Forest sciences Computer science » Programming Computer science » Other Engineering » Other Environmental science » Earth science Environmental science » Ecology Environmental science » Other Researcher Profile First Stage Researcher (R1) Application Deadline 15 Nov 2026 - 22:59 (UTC) Country Belgium Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Horizon Europe - ERC Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Department LA20 - Department of Environment

Degree MSc degree (or equivalent) in a relevant field (Bioscience-Engineering, Remote Sensing, Surveying, Physics, Geosciences, Environmental Sciences, Computer Science, etc.)

Occupancy rate 100%

Vacancy type Research staff

ABOUT GHENT UNIVERSITY

Ghent University is a world of its own. Employing more than 15.000 people, it is actively involved in education and research, management and administration, as well as technical and social service provision on a daily basis. It is one of the largest, most exciting employers in the area and offers great career opportunities.

With its 11 faculties and more than 85 departments offering state-of-the-art study programmes grounded in research in a wide range of academic fields, Ghent University is a logical choice for its staff and students.

YOUR JOB

Q-ForestLab ( www.q-forestlab.ugent.be ) is seeking a highly motivated PhD candidate to work on the ERC project SPACETWIN: Digital twins for understanding forest disturbances and recovery from space.

Forests worldwide are undergoing large-scale and unprecedented changes in terms of structure and species composition due to anthropogenic disturbances, climate change, and other global change drivers. Climate, disturbances and forest structure are all closely linked: changes in climate can lead directly to physical changes in forest structure and vice versa or to an anticipated increase in forest disturbances. However, it is still uncertain how forest structure is impacted by disturbances (locally) and how we can detect and monitor various levels of disturbance regimes using spaceborne satellite data (globally).
This PhD position draws on the experience and resources from multiple projects such as SPACETWIN (ERC) and the Queensland Permanent Rainforest Plots (QPRP) Australia project (FWO). This project will focus on the impact of drought, fire, cyclone and logging disturbances across a range of tropical and temperate forest ecosystems. It will lead to a step-change in our ability to observe, quantify and understand forest disturbances and recovery by using time series of the most detailed structural and radiometric 3D forest models ever built: 'digital twin' forests. The project will draw upon Earth observation platforms spanning passive and active optical and microwave sensors.

Within your work, you will:

• use 3D terrestrial laser scanning data to further develop the creation and implementation of digital twins of forests for optical and microwave radiative transfer modelling
• explore the near-real time inversion of remote sensing of forest disturbances from satellites
• use a library of digital twin forests to understand the monitoring of disturbances from satellites, applying this to important satellite missions quantifying forest structure such as ESA BIOMASS, NASA GEDI and future NASA EDGE and JAXA MOLI, with a particular focus on upscaling and uncertainties. We will use the extensive multi-scale lidar dataset of the Australian Wet Tropics as a case study. Fieldwork at these field sites may be required.

This PhD project will be promoted by prof. Kim Calders and co-supervised by dr. Niall Origo. You will interact regularly with Prof. Mathias Disney (University College London), who will act as an informal advisor.

WHAT WE ARE LOOKING FOR

• MSc degree (or equivalent) in a relevant field (Bioscience-Engineering, Remote Sensing, Surveying, Physics, Geosciences, Environmental Sciences, Computer Science, etc.)
• Fluent in programming languages such as (at least one of) R, C++ or Python
• Prior experience with satellite remote sensing (either optical or radar or both) and satellite data analysis
• Prior experience with, or a solid understanding of, radiative transfer modelling within vegetation is preferred
• Previous experience with (terrestrial) laser scanning and 3D modelling of forests is a plus, but not a requirement
• A passion for nature and forests and a keen interest in forest ecology. You might also contribute to fieldwork to collect additional field data for this project
• A good command of English, both written and oral.

WHAT WE CAN OFFER YOU

• We offer a full-time position