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PHD opening

Doctoral fellow - Department of Environment

Ghent University · LA20 - Department of Environment · Belgium

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About this position

Last application date

Nov 15, 2026 23:59

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 as a doctoral fellow, consisting of an initial period of 12 months, which, after a positive evaluation, will be extended to a total maximum of 48 months.

Your contract will start on January 1, 2027 at the earliest.

The fellowship amount is 100% of the net salary of an AAP member in equal family circumstances. The individual fellowship amount is determined by Team Personnel Administration based on family status and seniority. A grant that meets the conditions and criteria of the regulations for doctoral fellowships is considered free of personal income tax.

Click here for more information about our salary scales.

All Ghent University staff members enjoy a number of benefits, such as a wide range of training and education opportunities, 36 days of holiday leave (on an annual basis for a full-time job) supplemented by annual fixed bridge days, bicycle allowance and eco vouchers. Click here for a complete overview of all the staff benefits.

The successful candidate will be based in Ghent.

Opportunities to present your work at scientific conferences.

Collaboration in a young and dynamic international scientific team. Life-work balance is important for us.

Please mention your favourite animal in your motivation letter so we can check that you at least read this vacancy in full.

About Q-ForestLab –

www.q-forestlab.ugent.be

Our lab is part of Ghent University in Belgium and studies vegetation dynamics, ecology, biogeochemistry, carbon and water cycling of terrestrial ecosystems. Q-ForestLab has a strong focus on forest ecosystems, but a broad interest in all types of terrestrial ecosystems, including tropical and temperate forests, drylands, peatlands and urban ecosystems. Our strength lies in a unique combination of methodologies,

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