Société Française de Bioinformatique

Mots-Clés

Enzyme Sequence annotation Ontology Benchmark Dataset Deep Learning

Description

The BioGraphs (formerly Dyliss) research team invites applications for a Scientific Engineer position in Rennes to contribute to the ECxit project (Exiting the EC Classification for Better Enzyme Annotation by Deep Learning).
The project aims to develop a novel hierarchical classification of enzymatic functions and apply deep learning approaches based on protein language models to improve enzyme function prediction and annotation.

Key responsibilities:

• Design and implement a hierarchical classification system integrating data from biological knowledge bases such as GO, EC, CAZy, Rhea, Reactome, BioCyc, KEGG, …
• Develop high-quality datasets for enzyme function prediction
• Evaluate deep learning methods
• Collaborate with biologists and machine learning researchers to deploy an annotation tool for the scientific community

Required qualifications:

• Master’s degree or Engineering degree in Bioinformatics, Computational Biology, or related fields
• Knowledge of enzyme biology, enzymology, metabolism, and protein function annotation
• Experience with Python and scientific data processing
• Familiarity with biological databases and ontology-based data representation

Full position description and application: https://recrutement.inria.fr/public/classic/en/offres/2026-10190