Bioinformatics / Computational Biology

In order to apply to one of the programmes of the master, please select the programme you are interested in under the "Year 1 & 2 Master's programmes" tab.

Learning outcome targets

The Master’s degree in Bioinformatics focuses on the high-level research and development requirements of companies and research organisations in bioinformatics, biostatistics and biotechnology, as well as life sciences and agronomy.
This multidisciplinary course responds to this demand by educating specialists at the interface of biology, agronomy, computer science and mathematics meet.
By the end of the Master’s degree, students will have mastered several programming languages, a number of algorithmic methods and artificial intelligence methods, as well as statistics and databases for storing and analysing genomic and biomedical Big Data. They will have also studied bioinformatics (systems biology, structural biology, functional genomics, etc.) and be able to apply the methods learned to a variety of projects in life and medical sciences, as well as environmental sciences and agronomy.
They will be able to:
(i) understand current issues around the complexity and diversity in biology and life sciences in general, perform well in research and innovation in areas of academic research, the biotechnology industry, the agro-industry, pharmaceuticals and health;
(ii) meet new challenges resulting from the very rapid evolution and development of technology which produces high-speed data from multiple and disparate sources;
(iii) master information technology associated with the analysis and modelling of biomedical data;
(iv) independently take charge of application development projects in multi-disciplinary contexts in various programming languages, proposing innovative IT solutions and carry out the analyses and developments necessary to test new methods and hypotheses concerning living organisms.

Post-graduate profile

The aim of the Master in Bioinformatics is to facilitate entry into the working world, both in the academic field (research in laboratories or management of bioinformatics platforms), and in the private sector (biotechnology companies, pharmaceutical industries, food industries). Students can thus:
- access engineering jobs in bioinformatics, typically in R&D, for example: designer and developer of databases and websites in the fields of biology, health, agronomy or the environment; designer and developer of bioinformatics algorithms and software; biomedical data analyst; managers of bioinformatics platforms.
- or continue their studies, doing a thesis in Life and Health Sciences or in Informatics with a focus on bioinformatics, for instance in Paris-Saclay's Doctoral Schools: STIC (Information and Communication Sciences and Technologies), Interfaces, SDSV (Structure and Dynamics of Living Systems) and BioSigne (Signals and integrative networks in biology).

Transfer paths

Subject to the approval of the academic admission panels, there are transfer paths available at the end of the M1 between the BIBS programme and the GENIOMHE programme components.
Internship offers are shared among the two standard study paths.


The Bioinformatics Master's degree is aimed at a very wide audience with two complementary study paths in Computational Biology: Analysis, Modeling and Engineering of Biological and Medical Information (BIBS M1-AMI2B M2) and GENomics InfOrmatics and Mathematics for Health and Environment (GENIOMHE M1- GENIOMHE M2). The AMI2B M2 has a greater focus on algorithmic approaches and data science for the processing of biomedical Big Data, offering a range of options to tailor the curriculum according to personal preferences, while the GENIOMHE M2 provides training in English in the large-scale analysis of genomic data for genomic medicine, based particularly on advanced computational methods in algorithms, machine learning and AI, Big Data, amongst others.

Skills required within the Field of Study :
  • Choose, evaluate and optimise the various IT and statistical methods used in data science and artificial intelligence to analyse diverse, mass biological data.

  • Analyse a biological or biomedical problem and design a model or a solution for this problem using existing IT and statistical methods or by proposing new ones.

  • Be part of a project and work in a collaborative way by applying a method of project management.

  • Explain and present orally and in writing a project and scientific results in French and English.