Mots-Clés
omics
data integration
multitask learning
computer science
computational biology
multi-modal
deep learning
precision medicine
predictive model
Description
About Us
The Institut de Cancérologie de l’Ouest (ICO) is a leading cancer research and treatment center in France. Our multidisciplinary research team (Omics and Data Science Unit) - comprising bioinformaticians, mathematicians, a biologist, and a medical biologist - works at the intersection of data science, artificial intelligence, and biomedical research. We aim to develop cutting-edge computational tools to advance precision medicine for cancer patients.
Position Overview
We are seeking a highly motivated early-career postdoctoral researcher with a strong background in computer science, machine learning, or data engineering. No prior experience in biology is required.
The successful candidate will lead a project within PRECIZE, part of the SIRIC ILIAD program funded by INCa (French National Cancer Institute). The goal is to develop novel computational frameworks to predict and characterize high-risk relapse patients in the context of emerging therapeutic strategies. The position also involves collaboration with the LERIA laboratory (Laboratoire d’Études et de Recherche en Informatique d’Angers).
Key Responsibilities
• Design and implement scalable software pipelines for biological data analysis, with a focus on processing and modeling high-dimensional tabular data formats such as gene expression matrices.
• Develop machine learning models for classification, prediction, and feature extraction.
• Apply and adapt multi-task learning (MTL) techniques to integrate heterogeneous data sources.
• Collaborate with domain experts to interpret results and refine models.
• Contribute to publications and open-source tools.
Required Qualifications
• PhD in Computer Science, Machine Learning, Data Science, or related field.
• Strong programming skills (Python, R, or equivalent).
• Experience with Machine Learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
• Solid understanding of supervised learning, model evaluation, and optimization.
• Ability to work independently and in a collaborative research environment.
Desirable Skills
• Familiarity with multi-modal learning.
• Interest in biomedical applications or healthcare data.
• Experience with large-scale data integration or distributed computing.
Focus Areas
This position emphasizes:
• MTL: Designing models that learn shared representations across multiple related prediction tasks.
• Multi-omics data integration: combining transcriptomics, radiomics and/or proteomics data to uncover actionable insights in cancer biology.