PhD Student in Artificial Intelligence - « Interpretable Vector Symbolic Architectures for scalable Genome Interpretation »

Universite de Montpellier; France; Sur place; EUR 23 000 - 28 000; Plein temps

Résumé du poste

Universite de Montpellier invites applications for a PhD student position in the AI for Genome Interpretation program, embedded in the IGMM. The project investigates Vector Symbolic Architectures to predict phenotypes from genomic sequencing data, within an interdisciplinary and international environment.

The successful candidate will work at the interface of AI, ML, and genomics, building novel interpretable models and contributing to methodological and biological insights.

Qualifications

• Master's degree or equivalent in Computer Science, AI, ML, Bioinformatics, or related field.
• Strong Python programming skills and PyTorch experience.
• Good knowledge of machine learning and statistical learning.

Responsabilités

• Develop multi-scale Vector Symbolic Architectures representations for genomic data.
• Design supervised and self-supervised learning methods on genomic hypervectors.
• Benchmark VSAs against traditional ML and deep learning approaches.
• Develop interpretable decoding methods to quantify variant-, gene-, and pathway-level contributions.
• Apply methods to yeast and human genomic data and analyze biological relevance.

Connaissances

Python programming

PyTorch

Machine Learning

Bioinformatics

Formation

Master's degree or equivalent

Outils

Python

PyTorch

Description du poste

Organisation/Company Universite de Montpellier Department Human Resources Research Field Computer science Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 16 Oct 2026 - 23:59 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Nov 2026 Is the job funded through the EU Research Framework Programme? Other EU programme Reference Number 2026-D0208 Is the Job related to staff position within a Research Infrastructure? No

Offer Description

- work environment: The recruited person will join the « AI for Genome Interpretation » directed by Daniele Raimondi at l’IGMM. The project will be developed in an interdisciplinary and international environment .

The core focus of D. Raimondi’s “AI for Genome Interpretation” lab at the Institut de Génétique Moléculaire de Montpellier (IGMM) is the development of tailor-made Machine Learning (ML) and Neural Networks (NNs) models for Genome Interpretation (GI)1,2. GI is the umbrella term describing the bioinformatics approaches devoted to understanding the relationship between genotype and phenotype, targeting open biological problems ranging from human clinical genetics to plant biology. My aim is to develop a new paradigm of computational methods to overcome the limitations of current GI algorithms used in quantitative and clinical genetics. To do so, I propose innovative and interpretable tailor-made frameworks of Artificial Intelligence (AI) methods to combine genomics data and contextual knowledge of biological processes, with the goal of modeling how the information encoded in our genome leads to the observed phenotypes. My overarching goal is to enable novel applications of Explainable AI (XAI) in genetics3,4,5, precision medicine, drug discovery6 and agricultural technology7.

Raimondi’s lab has an ongoing collaboration with Dr. C. Trottier and X. Bry at IMAG, since they are experts in statistical methods such as Linear Mixed Models (LMMs), which are one very often used in different fields of GI, including quantitative genetics, and plant and cattle breeding programs. In the context of this collaboration, Raimondi, Trottier and Bry are currently co-supervising a post-doc funded by the AISSAI shared initiative between Google and CNRS. This project already led to the creation of a pytorch library for the differentiable training of LMMs, with a paper in preparation as well.

- main mission:

We are looking for a motivated PhD student to work at the interface of Artificial Intelligence, Machine Learning, Bioinformatics, and Genomics. The project will investigate the use of Vector Symbolic Architectures (VSA), also known as Hyperdimensional Computing, as a new framework for predicting phenotypes and disease risk directly from genomic sequencing data.

Genome Interpretation aims to understand how genetic variation determines phenotypes, including quantitative traits and disease risk. This remains a challenging machine-learning problem because genomic datasets contain millions of variables but comparatively few samples, making conventional neural networks prone to overfitting and difficult to interpret.

The PhD project will develop a new approach based on Vector Symbolic Architectures, in which genomic information is represented using high-dimensional vectors and compositional algebraic operations.

The student will develop methods to encode genomic information at multiple biological scales:

using operations such as binding, bundling, and permutation. These representations will then be used to build predictive models of genotype–phenotype relationships.

The project will first be prototyped using yeast whole-genome sequencing data and hundreds of quantitative phenotypes, allowing rapid comparison of different VSA representations and learning strategies. The methods will subsequently be applied to human exome sequencing data, using publicly available case-control cohorts.

A major component of the project will focus on interpretability. The student will develop methods based on VSA decoding and unbinding to identify the variants, genes, and biological pathways contributing to individual predictions and compare the discovered signals with known disease-associated loci.

The project therefore combines methodological development in machine learning with applications to real genomic datasets and clinically relevant problems.

- activities:

The PhD student will:

• develop multi-scale VSA representations for genomic sequencing data; investigate different hypervector representations, including binary, bipolar, ternary, and continuous encodings;
• design supervised and potentially self-supervised learning methods operating on genomic hypervectors;
• benchmark VSA models against conventional machine-learning and deep-learning approaches;
• develop interpretable decoding methods to quantify variant-, gene-, and pathway-level contributions;
• apply the developed methods to yeast genotype–phenotype prediction and human IBD disease-risk prediction;
• analyze the biological relevance of the associations discovered by the models;
• publish the methodological and biological results in international journals and conferences.

The gross monthly salary is €2,300

Contract duration: 3 years

Contract date from 01/11/2026 to31/10/2029

Requirements

Research Field Computer science Education Level Master Degree or equivalent

Skills/Qualifications

Candidates should have a Master's degree, or equivalent, in Computer Science, Artificial Intelligence, Machine Learning, Bioinformatics, Computational Biology, Applied Mathematics, or a related discipline.

Strong candidates should have:

• solid Python programming skills and pytorch;
• bioinformatics or computational genomics knowledge;
• good knowledge of machine learning and statistical learning;
• familiarity with linear algebra, vector representations, and optimization;
• experience working with scientific datasets;
• ability to independently design, implement, and evaluate computational methods;
• good written and spoken English.

Useful but not mandatory experience

Experience in one or more of the following would be advantageous:

• deep learning and PyTorch;
• hyperdimensional computing or Vector Symbolic Architectures;
• genomic data formats such as VCF;
• dimensionality reduction and representation learning;
• interpretable or explainable machine learning;
• high-performance computing and large-scale data analysis.

Previous biological training is not required, provided the candidate is interested in learning the necessary genomics and genetics concepts.

Candidate profile

We are particularly interested in candidates who enjoy developing new machine-learning methodology rather than only applying existing models. The project requires a combination of algorithmic thinking, mathematical reasoning, programming, and curiosity about biological problems.

The successful candidate will work on a highly interdisciplinary project at the frontier between AI and genome biology, with the opportunity to develop a largely unexplored computational paradigm for interpretable genome analysis.