[SocBiN] open phd position in bioinformatics / machine learning in Montpellier

[Daniele Raimondi]

*_Please feel free to share this offer for a PHD position at IGMM in 
Montpellier_

Project:*DNA large language models for end to end genome interpretation

_*Motivation*_

Interpreting the genome means modeling the relationship between genotype 
and phenotype, which is the fundamental goal of biology. Achieving this 
would revolutionize genetics, medicine, and agro-tech. In clinical 
genetics, it could lead to personalized treatments tailored to each 
patient's genome, enabling precision medicine.

_*Objectives*_

This project fuses quantitative genetics with bioinformatics and 
cutting-edge Artificial Intelligence, using the latest Deep Learning 
Large Language Models for DNA to advance our ability to predict the 
phenotypes deriving from the observed genotypes. This project is based 
on the previous work of Dr. Raimondi on Genome Interpretation (GI) for 
the prediction of clinically relevant phenotypes in humans.

Dr. Raimondi previous works focused on encoding Whole-Exome/Whole-Genome 
Sequencing (WES/WGS) data into compact, machine-readable features, while 
reducing overfitting caused by high-dimensional genomic data. However, 
to limit complexity, we had to rely on coarse gene-level summaries, such 
as mutational burden per gene, which sacrificed fine-grained genetic detail.

The goal of this project is to move beyond these gene-centric encodings 
by building neural network architectures that operate directly at the 
nucleotide level. To achieve this, we propose integrating pre-trained 
DNA LLMs as unsupervised feature extractors within GI models. These 
models, trained using self-supervised learning on entire genomes, 
capture rich patterns of DNA dependencies and can produce 
information-dense latent representations.

DNA LLMs have shown strong performance in various functional genomics 
tasks, such as identifying regulatory elements and variant effects. This 
project will evaluate whether their latent representations can improve 
phenotype prediction. The new DNA LLM methods will be prototyped on 
A.thaliana, which is a well known model organism. Later developments 
will be translated to the disease risk prediction of Inflammatory Bowel 
Disease (IBD). Unlike DNA LLM research, this work applies LLMs to the 
interpretation of individual-level WES/WGS data for disease risk, 
marking a novel use of these models in human genetic prediction.

_*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 data science 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 *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 and 5) ability 
to *multi-task*across different projects. A good understanding of the 
basic concepts of Bioinformatics is not necessary but welcome. The 
project will be based on the development of *un-orthodox Neural 
Network*models with *Pytorch*.

B2 level of English is *required*.

The offer provides an initial 6-month contract, with possibility of 
renewal to 2 years. This project can be extended to 3 years and offers 
the opportunity to obtain a PhD.

_*Research environment*___

The recruited person will join the “AI for Genome Interpretation” team 
led by Dr. Daniele Raimondi at IGMM. The work will be conducted in an 
international (English-speaking) and interdisciplinary environment.

The Institute of Molecular Genetics of Montpellier (IGMM) is a joint 
research unit affiliated with the CNRS and the University of 
Montpellier. It comprises around 200 members, organized into 18 research 
groups, 9 shared support services and 9 technological and scientific 
platforms.

IGMM is a multidisciplinary institute whose research has both 
fundamental and translational impact in molecular and cellular biology 
at the international level.


_*Qualifications*_

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/Linux environment and 5) ability 
to *multi-task*across different projects. A good understanding of the 
basic concepts of Bioinformatics is not necessary but welcome. The 
project will be based on the development of *un-orthodox Neural 
Network*models with *Pytorch*. B2 level of English is required. 
Familiarity with scientific computing and libraries such as numpy, 
scikit-learn, scipy, pytorch.


*HOW TO APPLY:* 
https://umontpellier.nous-recrutons.fr/poste/7sqwj9n4vg-ingenieur-en-calcul-scientifique-ou-ingenieur-en-ingenierie-logicielle-fh/


-- 

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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