Educational mission

Teaching

DSIMB is not just a research unit, it's a teaching hub training the next generation of bioinformaticians and computational biologists.

Our team members teach courses in structural bioinformatics, molecular dynamics, machine learning, and in silico drug design at multiple partner institutions.

Main program

Master Bioinformatique

A rigorous, interdisciplinary program at Université Paris Cité covering the three pillars of bioinformatics: Omics, Structural Bioinformatics, and Systems Biology. Taught in French and English.

The "Biologie Informatique" track is directed by Jean-Christophe Gelly, with DSIMB members contributing significantly to the teaching.

French + English 2 years (120 ECTS) Campus Grands Moulins
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94%
Success rate
92%
Employment rate
3 tracks
Biologie Informatique • ISDD • Platform Engineering
Graduate school

Translational Bioinformatics

DSIMB is a key player in the Translational Bioinformatics Graduate School at Université Paris Cité. This program creates a seamless pipeline from Master's to PhD, preparing students for careers at the intersection of computation and medicine.

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MSc
Master's programs
PhD
Doctoral training

Université Paris Cité

Additional Programs

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Licence (BSc)

Undergraduate courses in Biology, Bioinformatics, and Computer Science fundamentals.

Key topics taught by DSIMB

  • • Protein structure prediction
  • • Molecular dynamics
  • • Machine learning for biology

Université Paris Cité - EIDD

Denis Diderot Engineering School

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Engineering degree - Génie Biologique

5-year engineering curriculum combining biology, computational methods, and practical laboratory skills.

ENS Lyon

Master (MSc) - Biology

Advanced courses in computational biology and structural analysis.

Nantes Université

Master (MSc) - Bioinformatics

Collaborative teaching in structural bioinformatics methods.

University of Saint-Joseph

Beirut, Lebanon

International partnership for bioinformatics education.

Opportunities

Student research

We actively welcome Master's students for internships on ongoing research projects. Students gain hands-on experience with deep learning, molecular dynamics, and structural bioinformatics while contributing to funded research initiatives.

M2 internship topics

  • • Deep learning for protein-carbohydrate interfaces (SugarPred)
  • • Protein flexibility prediction with PLMs
  • • Knottin design using generative AI
  • • Blood group antigen modeling

What you'll learn

  • • Python programming for bioinformatics
  • • Deep learning frameworks (PyTorch)
  • • Molecular dynamics simulations
  • • Scientific communication & publication