M2 Bioinformatique, Biostatistiques et applications de l'intelligence artificielle

The skills acquired upon completion of the M2 BIBS-IA are aligned with the national framework of the Master in Bioinformatics (RNCP 38964).

• Understand current challenges related to complexity and diversity in biology and the life sciences in general, and contribute actively to research and innovation in the academic, pharmaceutical, biotechnological, agri-industrial, and healthcare sectors;
• Address the challenges arising from the evolution and development of high-throughput data production technologies, coming from multiple and heterogeneous sources;
• Master computational approaches and artificial intelligence to analyze, model, and interpret large-scale biomedical data;
• Lead bioinformatics projects independently, in multidisciplinary environments, by developing innovative solutions using appropriate computational tools and programming languages;
• Experiment with, develop, and test new methods to explore hypotheses in the life sciences, at the interface between fundamental and applied research.

The M2 BIBS-IA program is dedicated to the advanced research and development needs of companies and research organizations in bioinformatics and biostatistics.

This multidisciplinary training program meets these needs by preparing specialists at the interface of biology, computer science, and mathematics.

Upon completion of the M2 BIBS-IA, students master several programming languages, a wide range of artificial intelligence, algorithmic, statistical, and data science methods, enabling them to store, model, and analyze large-scale genomic and biomedical data. They also deepen their knowledge in key areas of bioinformatics (systems biology, structural biology, functional genomics) and apply the methods learned to diverse projects in life sciences and medicine, as well as in environmental sciences and agronomy.

The curriculum of the M2 BIBS-IA is organized into six major knowledge and skills blocks:

Block 1 – Management of large-scale data and optimization.
This block includes three teaching units focused on optimizing the processing of various types of data (relational databases and graph data). A project based on massive real-world datasets allows students to put their acquired knowledge into practice.

Block 2 – High-throughput genomics.
This block comprises two teaching units: one focused on comparative genomics and another on population genomics, with strong emphasis on the application of statistical methods in these domains.

Block 3 – Data analysis and AI for biology.
This block includes three teaching units, covering topics ranging from fundamental AI concepts such as machine learning models, to applications of AI in integrative biology and image analysis.

Block 4 – Scientific integrity and reproducibility.
This block comprises two pre-professional teaching units: a reprohackathon project and a unit supporting professional integration. Students acquire best practices in open science, scientific integrity, ethics, and energy efficiency (ecological impact of digital practices, particularly artificial intelligence).

Block 5 – Advanced applications of Artificial Intelligence.
Students select four teaching units within this block, which includes six dedicated to the M2 BIBS-IA track and focused on diverse applications of AI. Examples include a data camp (a machine learning hackathon on real-world datasets, jointly organized with École Polytechnique and Télécom Paris) and data mining in agronomy (jointly with AgroParisTech). In addition, students may choose elective units from other Master’s programs (Computer Science, Biology-Health, etc.).