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

[Daniele Raimondi]

*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_ 
<https://www.igmm.cnrs.fr/team/ia-pour-linterpretation-du-genome/>) 
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_ <https://www.montpellier-france.com/> 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_ 
<https://www.igmm.cnrs.fr/team/ia-pour-linterpretation-du-genome/>) 
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:

      o

        Linear algebra (vector and matrix operations)

      o

        Optimization methods

      o

        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


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