Job offer

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.

Project description

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:

variant * gene * pathway * individual

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

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;
• population genetics or genotype-phenotype prediction;
• 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.