University Degree in Artificial INtelligence and Systems Immunology (AINSI)

  • Science and Technology
  • Medicine and Health
University Degree in Artificial INtelligence and Systems Immunology (AINSI)

Résumé

To understand the AI methods applied to computational immunology and their applications (epitope design, diagnostics, personalised immunotherapy), whilst gaining proficiency in the bioinformatics tools and databases used for data analysis Read more

Objectifs

Executive Education Program / Accessible for resumption of studies /
Diplôme d'université
70 hours over 2 x 1 week sessions
École Universitaire de Recherche Sciences du Vivant et de la Santé / École Universitaire de Recherche Sciences du Vivant et de la Santé
Nice
English

Details

International expertise at the intersection of AI and immunology

Discover how artificial intelligence is revolutionising modern immunology. This course provides you with the essential knowledge to understand and apply AI methods in research, diagnosis and the development of next-generation immunotherapies.

Develop the skills needed for the future of medicine

Delivered entirely in English, the AINSI University Diploma is an intensive, career-focused course combining 70 hours of lectures and practical workshops spread across two intensive modules. The programme is designed to enable students to rapidly acquire skills that are directly applicable in the laboratory, in academic research or in industry.

Learn how to use bioinformatics tools, omics databases and AI approaches dedicated to computational immunology. Develop a unique skill set at the intersection of biology, data science and innovation in healthcare.

A project receiving financial support from FRANCE 2030

The AINSI University Diploma is financially supported by the French State as part of the ‘Skills and Professions of the Future’ Call for Expressions of Interest (AMI) under the France 2030 Programme, administered by the Caisse des Dépôts.


 

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Introduction

Artificial intelligence (AI) has become essential in immunology, enabling, for example, the prediction of antigens and neoantigens, the spatial and temporal dynamics of immune cells, and improvements in immunotherapeutic engineering.

To meet this need, the University Diploma in Artificial Intelligence and Systems Immunology (DU AINSI) offers a course, delivered entirely in English, spread over 10 days in two modules combining theoretical lectures and practical workshops, totalling 70 hours.

Through an exploration of AI tools and their applications in immunology, the programme aims to provide an understanding of AI methods applied to computational immunology (epitope design, immunological diagnostics, immunotoxicology and personalised immunotherapy approaches) and to equip participants with proficiency in simple bioinformatics tools and the use of databases for omics analyses in computational immunology.

Enrolment limited to 16 seats:

  • 10 participants from the private sector

  • 6 participants from the public sector (researchers and PhD students)

Specific objectives of the programme:

  • To detect immunological biomarkers and improve diagnosis, image analysis and flow cytometry through the application of AI
  • To model molecular interactions, personalise immunotherapy strategies and stratify patients (immuno-oncology and autoimmunity) through the use of integrated AI techniques
  • Predict and analyse the immunotoxic risks of drugs, vaccines and innovative therapies; identify transcriptomic signatures of immunotoxicity; and predict interactions between drugs and the immune system through the expert application of AI
  • Predict and select epitopes in vaccinology and immunotherapy, optimise immunogenicity and reduce immune evasion by pathogens and cells using AI principles
  • Master the analysis of large-scale immunological data to model the dynamics of the immune system and develop digital immune maps that predict the progression of inflammatory and infectious diseases

Conference room at the St Paul Hotel, in Nice (to be confirmed):

29 Bd Franck Pilatte, 06300 Nice 

  • Mme Claudine BLIN
    Director of the Laboratory of Molecular Physiomedicine (LP2M).
    Head of the Osteoimmunology, Niche Cells and Inflammation team.

    .

  • M. Phillipe BLANCOU
    Professor IPMC-INSERM
    Head of the ‘Immunity Regulation by the Nervous System’ Team

Partnership


MabDesign

French association of the industrial sector of biomedicine 
Rectorat d'Aix-Marseille ou de Nice  
Région Provence Alpes-Côte d'Azur
Université de Toulon
Aix-Marseille Université

Admission

Prerequisite

  • Scientific project managers
  • Researchers
  • Lecturers and researchers
  • Clinicians
  • PhD students
  • Design engineers
  • Research engineers

Conditions of applications

Effectif

Please note : enrolment is limited to 16 seats

- 10 participants from the private sector
- 6 participants from the public sector

How and when to register

Applications must be submitted from 15 September 2026 via the Université Côte d’Azur’ eCandidat platform.
If you encounter any difficulties with your registration, please consult the eCandidat user guide

egistration/application deadline: 29/11/2026 

f you have any specific questions regarding your registration, please do not hesitate to contact:  : 

Program

Module 1 – Introduction to Artificial Intelligence
  • Training by EFELIA
  • Theory and practice
Module 2 – AI and Immunodiagnostics
  • detection of immunological biomarkers (e.g., cytokines, autoantibodies, TCR/BCR sequencing, plasma proteins)
  • combining genetic and clinical and environmental data for health diagnostics and predictive modeling
  • diagnosis of autoimmune, infectious, and cancer-related diseases through deep learning
  • analysis of imaging and cytometry data
  • prediction of graft survival and the effectiveness of immunosuppressive therapies
Module 3 – AI and Personalized Immunotherapy
  • model molecular interactions
  • personalize immunotherapy strategies and anticipate responses to immune checkpoint inhibitors using AI
  • understand host-tumor interaction analyses through deep learning to optimize CAR-T therapies and anticancer vaccines
  • stratify patients using machine learning
  • predict graft survival and the effectiveness of immunosuppressive therapies
Module 4 – AI and Epitope Design
  • predict and select epitopes in vaccinology and immunotherapy
  • optimize immunogenicity and reduce immune evasion by pathogens and cells
  • use machine learning algorithms to predict B- and T-cell epitopes based on MHC-I/MHC-II interactions
  • leverage databases
Module 5 – AI and Immunomics
  • predict and analyze immunotoxic risks of drugs, vaccines, and innovative therapies (hypersensitivity, autoimmunity, cytokine storms)
  • identify transcriptomic signatures of immunotoxicity
  • predict immunotoxicity (deep learning and clustering models)
  • predict interactions between drugs and the immune system

Steering Commitee

Name Title Insitute email
Philippe BLANCOU Teacher-researcherr Philippe.BLANCOU@univ-cotedazur.fr
Claudine BLIN Researcher - Research director
Mikita ARTOMOV Teacher-researcher BROAD Institute (USA)
GWASP
Max Artyomov   Researcher - PhD WUST/LP2M
Jetbrains biolabs (Munich)
Oleg Shpynov Researcher - PhD WUST/LP2M
Jetbrains biolabs (Munich)


Teaching Team

Salvish GOOMANEE

Junior fellow

EFELIA

goomanee.salvish@gmail.com 

Full time

  • The first part of the training course will take place from Monday 25 to Friday 29 January 2027 at the St Paul Hotel, Nice
  • The second part will be held in June 2027 (details to be confirmed, subject to the experts’ availability)

What's next ?

Level of education obtained after completion

Degree equivalent to 4 or 5 years' higher education (Bac +4 & Bac +5)

RNCP URL of content

https://www.francecompetences.fr/recherche/rncp/38672  

Target activities / attested skills

The specific objectives of the University Diploma, corresponding to the learning outcomes set out in the
RNCP38672 BC07 Master’s in Life Sciences:

To analyse data from experiments in the field of life sciences

To set up and carry out a comprehensive biological data processing workflow by selecting and using appropriate mathematical, statistical and/or bioinformatics tools

To contribute to the analysis of research experiments conducted within a scientific laboratory by applying knowledge across the various fields of life sciences

Submission

Standard fee for Continuing Professional Development:

  • 6000€
  • Eligible for funding via the Professional Training Account (CPF)
  • Eligible for funding via an OPCO
  • The price includes meals and accommodation at the St Paul Hotel, Nice (29 Boulevard Franck Pilatte, 06300 Nice) 

Reduced fees:

  • Public sector researchers: €1,400
  • PhD students: €1,000
If you have any questions regarding funding, please contact Clara.Esnault@mabdesign.fr Tel : +33 (0)4 78 02 39 93