PhD position in bioinformatics and artificial intelligence to study tumor cell plasticity (M/F)

CNRS; France; On-site; EUR 34,000 - 42,000; Full time

Job summary

The CNRS Institute of Genetics and Development of Rennes (IGDR) invites applications for a First Stage Researcher PhD position in bioinformatics, focusing on tumor cell plasticity using single-cell data. The project combines regulatory network inference with AI approaches and multimodal data integration in a collaborative, interdisciplinary setting.

The PhD will be conducted at Rennes on the Villejean campus, benefiting from local computing resources and strong ties to AIMPACT partners.

Qualifications

• PhD-level candidate in genetics, bioinformatics or related field.
• Strong background in bioinformatics and cancer genomics.
• Experience with single-cell transcriptomics and Python or R.
• Ability to work in an interdisciplinary team.

Responsibilities

• Identify regulatory programs linked to tumor cell plasticity using the PDAC atlas.
• Integrate multimodal data (epigenomic and transcriptomic) to refine gene regulatory networks.
• Assess generalizability to other cancers with distinct plasticity landscapes.
• Collaborate within AIMPACT and contribute to data challenges on single-cell GRN inference.

Skills

Bioinformatics

Single-cell RNA-seq

Python

Machine learning

Genomics

Education

Enrolled in PhD program

Tools

SCENIC

GenOuest platform

Job description

Organisation/Company CNRS Department Institut de génétique et développement de Rennes Research Field Mathematics Researcher Profile First Stage Researcher (R1) Application Deadline 27 Oct 2026 - 23:59 (UTC) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Dec 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The Institute of Genetics and Development of Rennes (IGDR; CNRS UMR6290) is a biological research institute located on the health campus of the University of Rennes. With approximately 200 members, the IGDR develops innovative and multidisciplinary approaches to better understand the fundamental mechanisms governing cell division, identity and plasticity. Its research notably relies on approaches at the interface of biology, mathematics, physics and computer science.
The PhD will be primarily conducted within the emerging BINGO team at the IGDR, which develops bioinformatics and statistical approaches for the analysis of complex genomic and transcriptomic data in cancer research. The project will also benefit from the expertise of the @OMICs team (OSS, INSERM) and AIMPACT consortium partners in artificial intelligence and modeling.
The PhD will be supervised by Yuna Blum and Thomas Derrien (emerging BINGO team, IGDR, CNRS) and Myriam Bontonou (@OMICs team, OSS, INSERM). The project will also benefit from regular interactions with Magali Richard (LIG, UGA, CNRS), with whom Yuna Blum coordinates the AIMPACT project.
The project will be conducted in Rennes, on the Villejean campus, and will benefit from local computing and data-storage infrastructures, including the GenOuest and Eskemm bioinformatics platforms.

Summary

Tumor cell plasticity, i.e. the ability of tumor cells to switch between transcriptional states in response to intrinsic or environmental signals, plays a major role in tumor progression and treatment resistance. Understanding the molecular mechanisms governing these transitions therefore represents an important challenge in oncology.
This PhD project aims to develop and apply bioinformatics and artificial intelligence approaches to identify gene regulatory networks (GRNs) involved in tumor cell plasticity using single-cell transcriptomic data. The project will primarily focus on pancreatic ductal adenocarcinoma (PDAC), for which we have developed an integrated scRNA-seq atlas comprising more than one million cells from over 200 patients.
The project will combine conventional GRN inference methods, such as SCENIC, with recent approaches based on foundation models for single-cell data. Multimodal data, particularly epigenomic data, will also be integrated to refine the inferred networks and improve the identification of regulators associated with transitions between tumor cell states.

Specific objectives :

• Identify regulatory programs associated with tumor cell plasticity. Using our PDAC single-cell atlas, the project will investigate transcriptional programs and transcription factors associated with the continuum between classical, intermediate and basal-like tumor states. Foundation model-based approaches will be compared with conventional GRN inference methods and with previously identified regulators.
• Integrate multimodal data to refine GRNs. Publicly available complementary datasets, particularly chromatin accessibility and other epigenomic data, will be integrated with transcriptomic data to provide additional biological evidence for inferred regulatory relationships and prioritize candidate regulators of tumor cell plasticity.
• Assess the generalizability of the developed approaches to other tumor contexts. Methods developed in PDAC may be applied to other cancers with distinct plasticity landscapes to assess their ability to characterize intermediate tumor states and their underlying regulatory programs.

This PhD project will be carried out within AIMPACT, which develops artificial intelligence approaches to characterize and predict tumor cell plasticity by combining gene regulatory networks, spatial transcriptomics and predictive modeling. The PhD student will directly contribute to AIMPACT activities on single-cell GRN inference and will also participate in the organization of the project's scientific data challenges.