PhD in Bioinformatics and Artificial Intelligence for Studying Tumor Plasticity
Develop AI-driven gene regulatory networks to decode tumor cell plasticity using single-cell and multimodal genomic data.
Best for:Best for bioinformatics PhD candidates with expertise in single-cell genomics, machine learning, and computational biology, seeking to apply AI to cancer research.
Overview
This PhD position at the Institut de Génétique et Développement de Rennes (IGDR) focuses on advancing bioinformatics and AI to uncover molecular mechanisms of tumor plasticity in pancreatic ductal adenocarcinoma (PDAC). The project leverages a unique single-cell RNA-seq atlas of over a million cells from 200 patients, integrating cutting-edge foundation models and multimodal data (e.g., epigenomics) to infer gene regulatory networks (GRNs). The candidate will contribute to the AIMPACT consortium, developing methods to characterize intermediate tumor states and their regulatory programs, with potential applications across cancer types. The work bridges computational biology, AI, and oncology, offering interdisciplinary collaboration with experts in bioinformatics, genomics, and AI.
What you'll do
- Develop and apply AI-driven methods to infer gene regulatory networks (GRNs) from single-cell transcriptomic data, focusing on tumor plasticity in PDAC.
- Integrate multimodal genomic data (e.g., epigenomics) to refine GRNs and identify key regulators of tumor state transitions.
- Contrast foundation-model-based approaches with traditional GRN inference methods (e.g., SCENIC) to evaluate their performance.
- Assess the generalizability of developed methods to other cancer types with distinct plasticity landscapes.
- Contribute to organizing AIMPACT data challenges and collaborate with interdisciplinary teams in bioinformatics, genomics, and AI.
- Publish findings in peer-reviewed journals and present results at international conferences.
- Participate in scientific discussions and workshops within the IGDR and AIMPACT consortium.
Requirements
Degree required: Master's degree (or equivalent) in bioinformatics, computational biology, genetics, computer science, or a related quantitative field.
Degree offered: PhD
Language: English; proficiency sufficient for academic research and collaboration (no formal test score required).
Instruction: French and English (supervision and thesis will be in English).
- Strong programming skills in Python, R, or similar languages for bioinformatics analysis.
- Experience with single-cell genomics data (e.g., scRNA-seq) and related tools (e.g., Seurat, Scanpy).
- Familiarity with machine learning and AI techniques, particularly foundation models.
- Knowledge of gene regulatory network inference methods (e.g., SCENIC, ARACNE).
- Ability to handle and integrate multimodal genomic datasets (e.g., transcriptomics, epigenomics).
- Experience with high-performance computing or cloud-based bioinformatics platforms.
- No specific nationality restrictions; open to international applicants.
- No age limit.
- No prior experience cap, but relevant academic or research background is preferred.
Funding & terms
How to apply
Deadline: 2026-10-27
Applicants must submit their CV, a cover letter detailing their research interests and motivation, and a research proposal outlining their approach to the project. Additional documents may include academic transcripts and letters of recommendation. Applications should be sent via the CNRS employment portal.
- CV
- Cover letter
- Research proposal
- Academic transcripts (BSc and MSc)
- Letters of recommendation (2-3)
Contact
Yuna Blum· PhD Supervisor
Create a free profile to unlock your personal match score, the full requirements, funding details and how to apply — in English or Persian.