Research engineer in bioinformatics: inference of metabolic functions from metabarcoding data

European Commission; France; Sur place; EUR 60 000 - 80 000; Plein temps

Résumé du poste

A leading French research institution is seeking a Bioinformatics Engineer for the HyLife project. This role involves extending computational pipelines for metabolic functions linked to hydrogen storage risks. Ideal candidates will hold a master's degree in Bioinformatics and possess strong skills in Python programming and metabolic network modeling. This position offers flexible work hours, telework options, and numerous benefits including public transport reimbursement and generous leave policies.

Prestations

Partial reimbursement of public transport costs

7 weeks of annual leave + 10 extra days off

Possibility of teleworking

Professional equipment available

Social and cultural activities

Qualifications

• Master degree or engineering degree in Bioinformatics required.
• Working knowledge of metabolic network modelling is essential.
• Proficiency in Python programming is necessary.

Responsabilités

• Extend the Python pipeline Tabigecy for hydrogen metabolism.
• Apply Tabigecy to HyLife project samples.
• Participate in reporting results and writing project deliverables.

Connaissances

Bioinformatics

Metabolic network modelling

Python programming

Good relational skills

English proficiency

Formation

Master degree or engineering degree in Bioinformatics

Description du poste

Inria, the French national research institute for the digital sciences

Organisation/Company Inria, the French national research institute for the digital sciences Research Field Computer science Researcher Profile First Stage Researcher (R1) Country France Application Deadline 30 Sep 2025 - 00:00 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 38.5 Offer Starting Date 1 Oct 2025 Is the job funded through the EU Research Framework Programme? Horizon 2020 Reference Number 2025-09266 Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The engineer will work within the framework of the European project HyLife (https://hylife-cetp.com/ ) in the MICROCOSME team at Inria – Univ Grenoble Alpes. MICROCOSME is an interdisciplinary team that includes applied mathematicians, computer scientists, computational biologists as well as experimentalists from the microbiology/biophysics team BIOP of the Université Grenoble Alpes. The work will be carried out in collaboration with Arnaud Belcour, Hidde de Jong, and Delphine Ropers from MICROCOSME, as well as other members of the HyLife consortium from France, Germany, Norway and Czech Republic.

Keywords: Bioinformatics, inference of metabolic function, environmental microbiology, energy storage

Title: Bioinformatics analysis of microbial risks associated with hydrogen underground storage in Europe

Context and motivation: The transition from fossil fuels to renewable energy sources is one of the most important steps to mitigate climate change and build a sustainable energy system. The conversion of excess energy from solar and winds plants into hydrogen gas (H2) is a promising solution that requires large-scale underground gas storage for later use. Underground storage sites—such as salt caverns, aquifers, and former gas reservoirs—naturally harbour microbial communities. Some microbes metabolize hydrogen and can convert it into toxic gases like hydrogen sulphide (H2S). These microbial activities could lead to H2 losses and risks to operational safety and deterioration in H2 quality [1]. To date there is little understanding of the mechanisms of microbial H2 consumption and the effect of microbial growth on gas storage efficiency. The HyLife project aims to produce valuable insights into the types of microbes present and how they could influence stored H2 through extensive sampling of potential storage sites all over Europe. Inferring the metabolic functions of microbial communities from taxonomic information could help characterize the microbial activity and estimate the associated risks. As a first step towards this goal, we have developed a pipeline called Tabigecy [2], which exploits taxonomic affiliations to predict metabolic functions constituting biogeochemical cycles and analyse their environmental impact in underground storage sites.

Description: The goal of the proposed work is to extend the Tabigecy pipeline towards hydrogen metabolism and to apply the pipeline to the samples obtained in the HyLife project. The ultimate objective is to map the metabolic functions of the sampled microbial communities onto biogeochemical cycles, and to evaluate the potential impact of these communities on H₂ in the various storage sites. One challenge is the lack of characterisation of microorganisms in the underground, as this affects the quality of predicted metabolic functions. Another challenge lies in the high computational cost of inferring metabolic functions due to the large number of samples (>100).

References:

[1] Dopffel, Jansen & Gerritse (2021). Microbial side effects of underground hydrogen storage–Knowledge gaps, risks and opportunities for successful implementation. International Journal of Hydrogen Energy, 46(12), 8594-8606.

[2] Belcour et al. (2025) Predicting coarse-grained representations of biogeochemical cycles from metabarcoding data. Bioinformatics, 41 (Supplement 1), i49–i57

The proposed project involves:

• Understanding hydrogen metabolism from literature and textbooks
• Extending the Python pipeline Tabigecy with new metabolic functions involved in hydrogen consumption and production
• Applying Tabigecy to the numerous samples sequenced by the experimentalist partners of HyLife
• Participating in reporting results, by helping to write articles and project deliverables and by communicating results to HyLife partners during on-line and on-site meetings.

Good relational skills and English skills are important to work in an interdisciplinary and international environment.

Specific Requirements

The candidate is expected to hold a master degree or engineering degree in Bioinformatics, with a working knowledge of metabolic network modelling and programming skills in Python.

• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities