[SocBiN] open post-doc position in bioinformatics / machine learning in Montpellier

[Daniele Raimondi]

Dear colleagues,

please feel free to share the offer below for a 18 months post-doc in 
Bioinformatics/Machine Learning at the IGMM (CNRS) in Montpellier (France).

**

*Postdoctoral Position – Mixed Effects Neural Networks for Genome 
Interpretation*

*

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 18 months. The project is a 
collaboration between IGMM and IMAG, at the interface of genetics, 
bioinformatics, statistics, machine learning and deep learning.

The projectInterpreting 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 GI NN 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 InterpretationI 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 plantsand humanphenotypes.

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 postdoc will:

  *

    Start by familiarizing with existing research and methods for genome
    interpretation (GI NNs, LMMs, GWAS).

  *

    Familiarize with the sequencing data

  *

    Develop and benchmark MENN prototypes on sequencing datasets
    (WES/WGS), starting first from model organisms and then working on
    disease risk prediction in humans.

_Candidate profile:_We are looking for a motivatedand curious candidate, 
with a strong passion for scienceand 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 learnnew skills, methods and concepts, and 2) to 
enjoy finding new solutionsin the face of new and unforeseen difficulties.

The ideal candidate has 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/Linuxenvironment.

A good understanding of the basic concepts of Bioinformatics is not 
necessary but welcome. The project will consist in developing 
un-orthodox Neural Network models with Pytorch.

At least the B2 level of English is required.

Skills requiredWe are looking for someone with:

  *

    Strong backgroundin neural networks, machine learning, linear
    algebra and an understanding of statistics.

  *

    Solid programming skills in Pythonand 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

_Practical details_

  *

    Location: IGMM, Montpellier (with joint supervision at IMAG).

  *

    Duration: 18 months.

  *

    Starting date: flexible, but the candidate must be selected
    beforethe end of 2025.

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.

You can apply from this link

*

emploi.cnrs.fr

Portail Emploi CNRS - Offre d'emploi - Postdoctoral Researcher – 
Mixed-Effects Neural Networks for Genome Interpretation (M/F) <#>

🔗 
https://emploi.cnrs.fr/Offres/CDD/UMR5535-SARADE-091/Default.aspx?lang=EN 
<https://emploi.cnrs.fr/Offres/CDD/UMR5535-SARADE-091/Default.aspx?lang=EN>

-- 
Daniele Raimondi, PhD
Chaire de Professeur Junior CNRS
AI for Genome Interpretation group
Institut de Génétique Moléculaire de Montpellier (IGMM)
1919 Route de Mende
34090 Montpellier, France

- obscurum per obscurius -
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