POSTDOCTORAL POSITION IN THE DEVELOPMENT OF MACHINE LEARNING and DEEP LEARNING METHODS GENETICS and BIOINFORMATICS

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

Are you a machine learning expert, proficient in programming with tensors and vectorial operations (pytorch, numpy)? Do you know the ins and outs of machine learning methods and you can build 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 strong background in the development of machine learning methods for bioinformatics.

Context: The position is based at the Institute of Molecular Genetics of Montpellier (IGMM, 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 Montpellier University, a vibrant 70,000 student population 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.

AI4GI 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 UKBiobank. The project aims at the wide-spectrum prediction of human phenotypes, unlocking new frontiers in clinical genetics, precision medicine, disease risk prediction, and Explainable AI on genomics data.

The candidate will:

• Start by familiarizing with existing research and methods for genome interpretation

• Familiarize with the sequencing data and its pre-processing

• Study how DNA LLM 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 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 the interpretation of neural network predictions and outputs, including concept-based activation and conterfactual analyses.

The project focuses on the development of new neural network architectures to perform inference on sequencing data.

Candidate profile

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 background 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

Practical details

• Location: IGMM, Montpellier

• Duration: 12 months.

• Starting date: flexible, but the candidate must be selected in the first half of 2026.
  
  
  

If you’re interested in working at the crossroads of AI, machine learning, bioinformatics and genomics - and in developing new methods rather than just applying existing ones - we’d like to hear from you.

Applications should be made at this link: 

https://emploi.cnrs.fr/Offres/CDD/UMR5535-SARADE-107/Default.aspx