Summer schools

Data preparation, analysis, and sharing. Use of simulation and modeling tools capable of generating predictions in the field of neuroscience.

This summer school introduces students to modern methods in neuroscience data analysis, computational modeling, and reproducible research. Participants will learn how to organize, process, and analyze large datasets from electrophysiological recordings (EEG, neural recordings) and brain imaging (MRI). Training covers Python-based scientific tools, signal processing, neural oscillation analysis, neural network simulation using platforms such as NEST and The Virtual Brain, and machine learning approaches. Through hands-on exercises based on published research, students will reproduce and adapt scientific analyses. The program aims to develop practical skills, scientific reasoning, and independence in neuroscience research using accessible open-source tools.

The school is organized every year in late August / beginning of September, open to PhD and MSc students. 

Link to 2026 edition

Theoretical and practical knowledge of biological rhythms and their integration into experimental research.

This summer school aims to provide Master’s and PhD students in biology with the scientific and practical foundations needed to integrate biological rhythms into their research. The program covers circadian clocks, seasonal rhythms, sleep, metabolism, reproduction, chronotherapy, and the effects of environmental and pathological disruptions such as light pollution, aging, and neurodegenerative diseases. Through lectures, research presentations, laboratory visits, and hands-on workshops, participants will gain experience with methods used to study biological rhythms. The school also promotes networking, interdisciplinary exchange, diversity, and environmentally responsible practices while highlighting the importance of rhythmicity in experimental research.

The school is organized on a yearly basis in September. It is open to PhD and MSc students.

Link to the 2025 edition