Brain Activity Analysis, Modeling & Behavior Study

Our research integrates two lines of inquiry. Anchored in the analysis of population activity and dynamics in brain-inspired neural networks, it moves in two complementary directions:

1. developing a unified mathematical framework to reveal how structural connectivity shapes neural responses and network dynamic;
2. leveraging this framework to identify the key structural features and dynamic mechanisms that support adaptive, flexible behaviors, and enable Multi-Task Learning.

Ultimately, our goal is to bridge connectivity–dynamics–behavior across scales, from microscopic to large-scale brain networks, investigating how the connectome and other biological constraints enable complex cognitive functions and behavior.

Our working styles

• Biologically‑constrained network connectivity – analytical & computational modeling of connectivity motifs (pairwise & triplet) and their impact on global dynamics.
• Neuronal computation & cognition – developing interpretable recurrent neural networks (RNNs) that mimic animal behavior and cognitive functions.
• Interdisciplinary synergy – blending mathematics, physics, neuroscience, and AI to build theory‑driven, data‑constrained models.

Current Open Positions

Open positions for PhD students are available in the following areas: theoretical/computational neuroscience, mathematics, physics, computer science, or related fields.

Candidates with the following skills are especially encouraged to apply:

• proficiency with machine learning frameworks such as PyTorch, JAX, or TensorFlow;
• experience with recurrent neural networks and data-driven model fitting;
• background in dynamical systems or random matrix theory.

PhD Positions — Candidate Profile

I am looking for PhD candidates (with Master degree) who are excited to build theory at the interface of mathematics, physics, and neuroscience. The ideal applicant has:

• A solid mathematics / physics foundation — comfortable with Calculus, Linear Algebra, Probability & Statistics, and Ordinary Differential Equations, the everyday language of network dynamics and neural population models.
• Hands-on coding ability — proficiency in Python and at least one deep learning framework (PyTorch or JAX). Coursework in Numerical Analysis or other computational methods is a strong plus.
• A first taste of research — a computational-neuroscience-related project carried out during your Master’s studies (e.g., neural network modeling, data analysis of neural activity, or dynamical-systems modeling of the brain).

You do not need to tick every box — curiosity, mathematical maturity, and a genuine interest in understanding the brain matter most. If your background is interdisciplinary, all the better.

Why join us?

• Access to cutting‑edge data from the LJAD and NeuroMod;
• Collaborative environment spanning physics, mathematics, and biology;
• Build connections with a network of world‑class collaborators;
• Opportunities for interdisciplinary publishing and conference participation.

How to Apply

1. Prepare a CV (≤ 2 pages) and a brief statement of research interests (≤ 1 page).
2. Send your application to Yuxiu.SHAO(at)univ-cotedazur.fr.

All applications will be considered confidentially and without bias.

Join us in unraveling the mathematical secrets of the brain and building the next generation of bio‑inspired intelligence.

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