PhD Position in Theoretical and Computational Ecology: Forest Soil Ecosystem Stability, Carbon

Biological sciences » Biological engineering

Organisation/Company UNamur - Lab of

F. De Laender Department Biology Research Field Biological sciences » Biodiversity Physics » Biophysics Biological sciences » Biological engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Final date to receive applications 1 Aug 2026 - 12:00 (Europe/Brussels) Country Belgium Type of Contract Temporary Job Status Full-time Hours Per Week 38 Offer Starting Date 1 Oct 2026 Is the job funded through the EU Research Framework Programme?

Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

The University of Namur (Belgium) is seeking a highly motivated PhD candidate in theoretical and computational ecology to join the newly funded SHIFT project 'How does a shift in forest tree species composition affect soil organic carbon storage in ancient forests of Wallonia: site conditions, biotic interactions, and reversibility'.

This interdisciplinary project, led in collaboration with UNamur, UCLouvain, and ULiège , investigates how historical forest conversion and ecological restoration affect soil biodiversity, carbon storage, and ecosystem functioning in ancient forests of Wallonia.

The PhD position at the University of Namur will focus on the modelling and integration components of the project. The successful candidate will use empirical data from a large-scale field campaign across ancient forest stands to reconstruct soil ecosystem carbon budgets and develop dynamic models of complex soil ecosystems based on the literature . The goal is to understand how changes in tree species composition affect the functioning, stability, and reversibility of forest soil ecosystems.

More specifically, the PhD candidate will contribute to:

• integrating data on soil carbon pools and fluxes, microbial communities, soil fauna, and ecosystem functioning;
• estimating carbon budgets of forest soil ecosystems using quantitative and probabilistic methods;
• developing dynamic models to study ecosystem stability, resilience, and the possibility of irreversible change;
• investigating how biodiversity and soil conditions jointly determine whether restored ecosystems can return to the state of unconverted ancient forests;
• collaborating closely with researchers in forest ecology, soil science and microbial ecology across the three partner institutions.

This position is particularly suitable for candidates interested in:

• theoretical ecology and complex ecosystems ,
• ecological stability and resilience ,
• ecological networks and dynamical systems ,
• the use of quantitative methods to address major environmental questions .

The PhD candidate will be based in the Research Unit in Environmental and Evolutionary Biology (URBE) at the University of Namur, under the supervision of Prof. Frederik De Laender . The lab offers a stimulating international research environment with strong expertise in biodiversity–ecosystem functioning, coexistence theory, ecological stability, and complex ecological networks.

The project offers a unique opportunity to work at the interface of theory, computation, and empirical ecology within an ambitious collaborative framework, and to contribute to fundamental questions on how ecosystems respond to disturbance and restoration in a changing world.

Research Field Mathematics » Applied mathematics Education Level Master Degree or equivalent

Research Field Physics » Statistical physics Education Level Master Degree or equivalent

Research Field Biological sciences » Biological engineering Education Level Master Degree or equivalent

Skills/Qualifications

• A Master’s degree (or equivalent) in Ecology, Environmental Science, Computational Biology, Theoretical Biology, Applied Mathematics, Physics, Statistics , or a related discipline.
• Strong interest in theoretical and computational ecology , especially in questions related to ecosystem stability, resilience, biodiversity, and ecological networks .
• Good quantitative and analytical skills.
• Experience with programming and data analysis (preferably in R ; experience with Python, Julia, Matlab, or similar is…