Postdoctoral researcher (M/F) in representation learning for multi-primer environmental DNA data from soil communities

CNRS - National Center for Scientific Research; France; On-site; EUR 36,000 - 48,000; Full time

Job summary

The postdoctoral researcher position at the Laboratoire d'Écologie Alpine (CNRS, Université Grenoble Alpes and Université Savoie Mont Blanc) invites applicants to develop representation learning methods linking soil community composition to environmental conditions using six markers and environmental data.

You will work within the BIOM team on multimodal data from ORCHAMP observatory and advance reproducible analyses.

Qualifications

• PhD in computational ecology, statistics, applied mathematics or artificial intelligence.
• Strong deep learning experience and knowledge of representation learning and ordination methods.
• Familiarity with eDNA and metabarcoding data is strongly recommended.
• Proficiency in Python; frameworks such as PyTorch or TensorFlow; scientific computing with large spatial datasets.
• Rigour in model validation and reproducibility; good level of scientific English.

Responsibilities

• Review literature on multimodal representation approaches and ordination methods in ecology.
• Set up reproducible analyses and assess available eDNA and environmental data.
• Link eDNA and environment marker by marker, comparing linear and non-linear approaches.
• Integrate multiple markers to study relationships and predict composition, trophic groups and diversity.
• Disseminate results through publications, presentations and open-source code.

Education

PhD in computational ecology, statistics, applied mathematics or artificial intelligence

Tools

Python

PyTorch

TensorFlow

Job description

Organisation/Company CNRS Department Laboratoire d'Écologie Alpine Research Field Mathematics Researcher Profile Recognised Researcher (R2) Application Deadline 4 Nov 2026 - 23:59 (UTC) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Dec 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

Offer Description

Soils host a considerable share of terrestrial biodiversity and perform essential functions (decomposition, nutrient cycling, carbon storage). Yet this biodiversity remains largely unknown: most organisms are difficult to observe, their environmental drivers and interactions are poorly understood, and soils are rarely integrated into biodiversity models. Environmental DNA (eDNA) metabarcoding makes it possible to survey several compartments from a single sample, but raises the challenge of jointly analysing massive, high-dimensional and heterogeneous data. The project builds on the ORCHAMP observatory, which monitors soil communities along elevation gradients in the French Alps with six markers (eukaryotes, bacteria, fungi, insects, oligochaetes, springtails), coupled with climate, soil, land-use and vegetation data. The postdoctoral researcher will develop representation learning methods to link community composition to environmental conditions, first marker by marker, then by integrating several markers to study how soil compartments covary beyond their shared response to the environment.

The postdoctoral researcher will:

• review the literature on multimodal representation approaches and ordination methods in ecology
• take stock of the available eDNA and environmental data and set up reproducible analyses
• link eDNA and environment, marker by marker, comparing linear and non-linear approaches on held-out samples
• integrate several markers to study relationships between soil compartments and predict composition, trophic groups and diversity
• disseminate results through publications, presentations and open-source code

These directions are indicative: the postdoctoral researcher is encouraged to propose their own approaches.

The position is based at the Laboratoire d'Écologie Alpine (CNRS, Université Grenoble Alpes and Université Savoie Mont Blanc), one of the birthplaces of environmental DNA metabarcoding, within the BIOM team. The postdoctoral researcher will be supervised by Sara Si-Moussi and Wilfried Thuiller, in an environment combining ecology, bioinformatics and artificial intelligence, with access to the computing resources of the laboratory and the university.

• PhD in computational ecology, statistics, applied mathematics or artificial intelligence; knowledge of community ecology and macroecology is an asset
• experience in deep learning (required), knowledge of representation learning and ordination methods
• familiarity with eDNA and metabarcoding data strongly recommended
• Python, PyTorch or TensorFlow, scientific computing, handling of large and spatial datasets
• rigour in model validation and reproducibility
• autonomy, interest in interdisciplinary work, good level of scientific English

Application file: CV, cover letter. Applications must be submitted exclusively through the CNRS job portal. For scientific questions, please contact Wilfried Thuiller or Sara Si-Moussi.

Additional comments

Funding through CPJ 2025 PROBIE of Sara Si-Moussi (LECA)