Postdoc Position in Computational Neuroscience

We are investigating the short-scale dynamics of learning. During a learning session, some forms of learning emerge during practice while others consolidate during brief rest periods. Your mission will be to model, analyze, and interpret these dynamics using MEG/EEG and behavioral data, in collaboration with our Lyon partners.

Responsibilities

• Design and run behavioral/MEG paradigms probing practice vs. rest learning.
• Develop analysis pipelines (Python, MNE, scikit‑learn, PyTorch welcome).
• Build computational models of learning and validate them on neural data.
• Collaborate with clinicians for translational aspects of the project.
• Write papers, present results, and contribute to open-source code.

Requirements

• PhD (or soon) in Neuroscience, Applied Math, CS, Physics, or related field.
• Strong quantitative skills and hands-on experience with MEG/EEG or similar.
• Solid Python stack (NumPy/SciPy/pandas; MNE appreciated).
• Familiarity with ML/statistics for time‑series; Bayesian mindset a plus.
• Excellent communication; team player; fluent in English.

What we offer

• Collaborative environment across Paris & Lyon with access to state‑of‑the‑art MEG/EEG.
• Mentoring and opportunities to supervise students.
• Funding for conferences, computing, and open science.
• Flexible start date (from September 2025).

If you have questions, contact us.