- Location: Narbonne, France
- Duration: 18 months
- Funding: ElectroMIC project (PEPR B-BEST, France 2030)
- Start Date: 01/03/2027
- Application Deadline: 01/12/2026
Job description and scientific context
Every year, the European Union produces between 118 and 138 million tonnes of biowaste, an abundant resource for the production of platform molecules for green chemistry. Chain elongation offers one route for valorising these resources by converting organic substrates into medium-chain fatty acids (MCFAs), such as caproate and caprylate. This process relies on mixed microbial communities, whose metabolic diversity enables the conversion of complex substrates under non-sterile conditions. However, predicting and controlling their activity remains challenging.
A key difficulty is to determine which microorganisms perform chain elongation and which metabolic pathways they use. Reverse β-oxidation, the main pathway involved in chain elongation, shares many reactions and enzymes with degradative β-oxidation. Genomic data can reveal the metabolic potential of microbial populations, but cannot by themselves establish which pathways are active or in which direction metabolic fluxes proceed. Genome-scale metabolic models (GEMs), combined with thermodynamic constraints, offer a way to connect genomic information to the metabolic activity of microbial communities.
The ElectroMIC project (PEPR B-BEST, France 2030) investigates chain elongation in electro-fermentation, where an electrode provides an additional means of influencing microbial metabolism. Experiments conducted at LBE have revealed changes in microbial populations and product profiles under polarisation that are not yet fully understood. In particular, hydrogen production, the distribution of fermentation products and the evolution of microbial communities raise questions about the pathways involved and the fate of electrons supplied by the cathode. The postdoctoral researcher will address these questions by developing genome-scale metabolic models constrained by thermodynamics and integrating them with the available experimental data, to better understand the mechanisms underlying chain elongation and identify ways to improve the process.
Key Objectives
Genome-scale metabolic modelling (GEM) is at the core of this position. Using the available metagenomic and experimental data (metabolites, current, microbial communities and gases), the postdoctoral researcher will develop a model of chain elongation in electro-fermentation, with three objectives:
- Build a community metabolic model. Reconstruct and curate GEMs of the main microbial populations, with particular attention to chain elongation pathways and energy conservation mechanisms, and assemble them with thermodynamic constraints and cross-feeding between populations.
- Understand how the selected community works. Focusing on the populations selected under polarisation, the model will discriminate between elongation and degradation by confronting the thermodynamically feasible direction of fluxes with the genomes, and explore butyrate and caproate production pathways, the fate of cathodic electrons and the drivers of elongation performance, up to the possibility of elongation driven directly by cathodic H2.
- Link microbial populations to process performance. Identify genomic markers of candidate chain-elongating populations to monitor their dynamics under different operating regimes.
This work aims to better understand and predict process performance, and to propose control strategies based on the main operating levers: applied potential, hydrogen supply and ethanol concentration. It may also serve as a basis for new projects and international collaborations.
Specific Tasks
- Reconstruct metagenome-assembled genomes and curate GEMs of the main populations (reverse β-oxidation, electron bifurcation, Rnf complex, odd-chain elongation).
- Assemble a thermodynamically constrained community model and integrate ethanol inhibition in dynamic simulations (dFBA).
- Confront model predictions with metabolite, current, gas and community data, and close the electron balance.
- Design discriminating experiments with the experimental team (applied potential, H2 supply, ethanol concentration).
- Identify genomic markers and contribute, with the experimental team, to developing qPCR assays.
- Publish results in international journals and present them at conferences.
What We Offer
- Cutting-edge research environment: work in a state-of-the-art laboratory equipped with the latest technologies in environmental biotechnology. The equipment is fully shared, so you will have access to all the facilities.
- Mentorship and training: receive guidance from leading experts in the field and access to a comprehensive training program.
- Collaborative network: engage with a dynamic team of researchers and industry partners, both nationally and internationally.
- Funding and resources: competitive stipend and funding for research-related expenses, including conference travel and publications.
- Career development: opportunities for professional development, including workshops, seminars, and networking events.
- Work atmosphere: You will be part of a team that takes their work very seriously. We pride ourselves on fostering a collaborative and supportive atmosphere where researchers and interns work together in a dynamic and friendly environment to drive innovative solutions for environmental sustainability.
- Great working conditions: 30 days of holidays per year (+ 15 if time recovery is affected); support for parenting; access to inexpensive collective restaurants; flexible schedule; access to on-site sports facilities; sunny weather.
- Salary (gross): 3000-3200 €/month (depending on your experience).
Specifically, the postdoc will strengthen expertise in community-scale metabolic modelling under thermodynamic constraints and gain first-hand experience of bioelectrochemical systems. The postdoc will have the support of experienced researchers and technicians, and will work in close interaction with the project’s PhD student. Supervision: Nicolas Bernet and Elie Le Quéméner.
Requirements
- Academic Background: PhD in systems biology, bioinformatics, quantitative microbiology or a related field.
- Skills: proven experience in genome-scale metabolic modelling (reconstruction, curation and analysis of GEMs; FBA, dFBA, COBRApy, CarveMe, gapseq or equivalent); proficiency in Python and/or R and in metagenomic data analysis (genome reconstruction, functional annotation).
- Assets: integration of thermodynamic constraints, anaerobic metabolism and electron bifurcation.
- Attributes: self-motivated, able to work both independently and as part of a team.
- Language: fluent use of English; knowledge of French is an advantage but not required.
- Motivation: enjoys open questions, interdisciplinary work and close dialogue with experimentalists.
Application Process
Interested candidates should submit the following documents to nicolas.bernet_at_inrae.fr and elie.le-quemener_at_inrae.fr by 01/12/2026:
- A detailed CV, including academic qualifications and research experience.
- A cover letter explaining their motivation for applying and their suitability for the position.
- Contact details for potential referees.
We look forward to receiving your applications and welcoming you to our team at INRAE-LBE, where your research will contribute to a sustainable future.
More information can be found in https://jobs.inrae.fr/en/ot-30859
