Maëlle Pomies

UMR IAM

Engineer / Dec 2025 to Feb 2027

Analysis of microbial‑diversity data from soil samples

My work focuses on analysing soil microbial diversity through the development of a fungal database suitable for functional‑inference approaches. I am establishing methodological frameworks for functional inference in fungal communities. This includes processing amplicon‑based metagenomic datasets, performing biostatistical analyses and interpreting the resulting patterns.

Pierre Verjans

UMR FARE

PhD student / Feb 2025 to Feb 2028

Mineralogical and microbial controls on the stabilization of organic matter in soils

Soil organic matter (SOM) underpins essential ecosystem functions, including biomass production, hydrological regulation and long‑term carbon storage. Microbial communities are key drivers of SOM dynamics: through decomposition, they mineralise part of the organic matter into CO₂, while transforming another fraction into microbial‑derived compounds that can stabilise through interactions with mineral surfaces. As a result, SOM cycling involves numerous interconnected processes whose magnitude is strongly modulated by pedoclimatic factors and land‑management regimes.

This PhD aims to investigate SOM cycling across nine experimental sites in mainland France, encompassing a wide range of soil types, climatic conditions and management practices.

Ana Paula Brandao

UMR FARE

PhD student / Nov 2025 to Oct 2028

Study and modelling of the mechanisms governing carbon and nitrogen use by soil microbial communities in terrestrial ecosystems

This doctoral project aims to elucidate how soil microbial communities utilise carbon and nitrogen during organic‑matter decomposition in agroecological contexts, and to adapt the mechanistic CANTIS model to conditions of nitrogen limitation. A series of controlled incubation experiments will be performed using ¹³C‑ and ¹⁵N‑labelled plant litter and soils from contrasting ecosystems, enabling the quantification of microbial C and N fluxes. The resulting datasets will enhance mechanistic understanding and contribute to the development of improved simulation and decision‑support tools for sustainable agricultural and forestry management.