Postdoctoral Position – Deep Learning models to predict phenotypes from genomics data (M/F)

CNRS; France; Sur place; EUR 40 000 - 50 000; Plein temps

Résumé du poste

A leading research institute in Montpellier seeks a motivated postdoctoral researcher to join the AI for Genome Interpretation group. The role involves developing neural networks with applications in genetics and bioinformatics, requiring strong programming skills in Python and a background in machine learning. The position is for 16 months, starting in early 2026, with initial contract duration of 6 months. Ideal candidates are eager to solve problems and have at least B2 English proficiency.

Qualifications

• Eager to learn new skills, methods, and concepts.
• Strong passion for science and scientific discovery.
• At least B2 level of English required.

Responsabilités

• Join the AI for Genome Interpretation group to develop Neural Networks.
• Collaborate on interdisciplinary projects in genetics and bioinformatics.
• Develop un-orthodox Neural Network models with PyTorch.

Connaissances

Strong background in neural networks

Solid programming skills in Python

Understanding of machine learning

Knowledge of linear/mixed models

Familiarity with GNU/Linux

Problem solving skills

Good communication and teamwork skills

Formation

Background in Bioinformatics/Computer Science

Outils

Python

PyTorch

scikit-learn

numpy

Description du poste

Organisation/Company CNRS Department Institut de génétique moléculaire de Montpellier Research Field Biological sciences » Biology Researcher Profile First Stage Researcher (R1) Country France Application Deadline 26 Dec 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Feb 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

We are looking for a motivated postdoctoral researcher to join the AI for Genome Interpretation (AI4GI) group at the IGMM (CNRS, Montpellier). The project is a collaboration between IGMM and IMAG, at the interface of genetics, bioinformatics, statistics, machine learning and deep learning.

Motivation:

Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology. Achieving this could revolutionize genetics, medicine, and agricultural technology, leading for example to the development of better crops, able to face the challenges posed by global warming.

Objectives:

This project is an interdisciplinary effort at the frontier between Biology (Genetics, Genomics), Bioinformatics, Artificial Intelligence (Neural Networks) and Statistics (LMMs). The aim is to join the Bioinformatics expertise of Dr. Raimondi on the development of Genome Interpretation Neural Networks methods and their application to relevant biological problems with the expertise of Dr. Bry and Dr. Trottier on the statistical inference of Linear Mixed Models (LMMs).

The project's goal is to develop a new breed of Mixed Effects Neural Networks (MENN) for Genome Interpretation that take the best from both worlds, merging the flexibility and power of NNs with the ability of LMMs to robustly learn from structured and noisy (non-i.i.d.) data, applying them on the prediction of both plants and human phenotypes.

These models will combine the flexibility of neural networks with the statistical robustness of linear mixed models to tackle one of biology's most fundamental questions: how do genetic variants determine phenotypes?

The successful candidate will join the new AI research team led by Daniele Raimondi at the Montpellier Institute of Molecular Genetics (IGMM, UMR5535 CNRS/University of Montpellier) for a 16‑month project (initial contract of 6 months).

IGMM is a multidisciplinary institute with a global scientific impact, both fundamental and applied, in molecular and cellular biology (www.igmm.cnrs.fr). The institute gathers over 200 people – researchers, engineers, technicians, and students – organized into 18 research teams, and benefits from shared services with other CNRS units on campus as well as state‑of‑the‑art technological and scientific platforms.

If you're interested in working at the crossroads of AI, statistics, and genomics—and in developing new methods rather than just applying existing ones—we'd like to hear from you.

Candidate profile

We are looking for a motivated and curious candidate, with a strong passion for science and for scientific discovery through the use and creation of new neural networks and machine learning methods.

Bioinformatics and Genome Interpretation are multi‑disciplinary and rapidly evolving fields. Therefore, the candidate is expected to: 1) be eager to continuously learn new skills, methods and concepts, and 2) to enjoy finding new solutions in the face of new and unforeseen difficulties.

The ideal candidate has background in Bioinformatics/Computer Science, with a very good: 1) python programming skills, 2) understanding of the mathematical foundations and principles of Machine Learning, Linear Algebra (vectorial and matricial operations, optimization), with a particular focus on Neural Networks, 3) problem solving skills, 4) familiarity with GNU/Linux environment.

The project will consist in developing un‑orthodox Neural Network models with Pytorch.

At least the B2 level of English is required.

Skills required

• Strong background in neural networks, machine learning, linear algebra and an understanding of statistics.
• Solid programming skills in Python and in scientific computing (PyTorch, scikit-learn, numpy, etc).
• Familiarity with GNU/Linux.
• Problem solving skills.
• Good communication and teamwork skills.
• Knowledge of linear/mixed models is a plus.
• Familiarity with GWAS, population genetics, or bioinformatics pipelines are a plus.
• Experience with the processing of genomic biological data (whole exome or genome sequencing) is a plus.

Additional comments

Location: IGMM, Montpellier (co-supervision at IMAG).

Duration: 16 months (initial contract: 6 months).

Starting date: flexible (beginning of 2026).