ENGINEERING POSITION IN THE DEVELOPMENT OF MACHINE LEARNING AND DEEP LEARNING METHODS FOR GENETICS AND BIOINFORMATICS (M/F)

Job Description

Missions

Did you study computer science, mathematics or physics and you are on the way to become a machine learning expert? Are you proficient in programming with tensors and vectorial operations (PyTorch, NumPy)? Do you know the ins and outs of machine learning methods and you like building neural networks from scratch? Do you enjoy developing new neural network architectures to solve non-conventional problems? This position might be for you.
We are looking for a motivated and curious candidate, with a background in the development of machine learning methods for bioinformatics.

We are looking for a motivated research engineer (IE) to join the AI for Genome Interpretation (AI4GI) group at the IGMM (CNRS, Montpellier) for 12 months. The contract can be renewed for an additional 36 months if the project passes the evaluation steps.

Activity

The candidate will:
• Start by familiarizing with existing research and methods for genome interpretation
• Familiarize with sequencing data and its preprocessing
• Study how DNA LLMs work and develop solutions to integrate them into the neural network architectures developed by the lab
• Focus on developing low-level solutions for the scalability of neural networks and large language models applied to whole-genome sequencing data
• Develop from scratch algorithms and neural network architectures for the prediction of structured outputs (i.e., trees, graphs)
• Implement and develop methods for interpreting neural network predictions and outputs, including concept-based activation and counterfactual analyses
The project focuses on the development of new neural network architectures to perform inference on sequencing data.

Your Profil

Skills

Bioinformatics and genome interpretation are multidisciplinary and rapidly evolving fields. We are looking for a candidate who:
• Has a background in computer science, mathematics, or physics, with a strong focus on machine learning
• Is eager to continuously learn new skills, methods, and concepts
• Enjoys tackling novel and unforeseen challenges with strong problem-solving skills
Required skills and expertise
• Strong interest in neural networks, machine learning, linear algebra, and a working understanding of statistics
• Deep understanding of machine learning foundations, including:
• Linear algebra (vector and matrix operations)
• Optimization methods
• Neural networks (with practical experience in PyTorch)
• Solid programming skills in Python and scientific computing (e.g., PyTorch, scikit-learn, NumPy)
• Proficiency with GNU/Linux environments (including tools such as SSH)
• Good communication and teamwork skills
Additional (preferred) qualifications
• Familiarity with GWAS, population genetics, or bioinformatics pipelines
• Experience processing genomic data (e.g., whole-exome or whole-genome sequencing)
• Basic understanding of genetics and biology
Other information
• The project involves developing unconventional neural network models using PyTorch
• A minimum English level of B2 is required
• Applications must be submitted in English

Your Work Environment

The position is based at the Institute of Molecular Genetics of Montpellier (IGMM UMR5535, CNRS), in a highly international and interdisciplinary research environment. Montpellier is a dynamic Mediterranean city with an exceptional environment, culture, and quality of life. It is home to numerous high-quality research institutes and the University of Montpellier, with a vibrant population of 70,000 students and one of the world's oldest medical schools.
The Lab: The work will be carried out in the AI for Genome Interpretation (AI4GI) group, led by Dr. Daniele Raimondi. The group focuses on the development of advanced artificial intelligence and machine learning methods for genome interpretation, with a particular emphasis on modeling the relationship between genetic variation and phenotypic outcomes.

The AI4GI lab develops tailor-made neural network architectures, including sparse and biologically informed models, to predict disease risk and complex quantitative traits from large-scale genomic data such as whole-genome and exome sequencing. By combining methodological innovation in AI with applications in human genetics, cancer genomics, and plant genomics, AI4GI aims to advance our understanding of genotype–phenotype relationships and precision medicine.
The project: This project aims at developing a new paradigm of General Genome Interpretation (GenGI) models by combining DNA Large Language Models (DLLMs) with Deep Neural Networks to predict human phenotypes directly from Whole Exome Sequencing samples from the UK Biobank. The project targets wide-spectrum prediction of human phenotypes, opening new directions in clinical genetics, precision medicine, disease risk prediction, and explainable AI on genomics data.

Compensation and benefits

Compensation

From 2521€ gross monthly salary, depending of experience

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

The research professions