Missions

The team led by Srdjan Ostojic at École Normale Supérieure Paris is recruiting a Research Assistant to study learning dynamics in Recurrent Neural Network (RNN) models of cognitive tasks.

Activités

- Implementation and analysis of RNN models in Python using deep learning frameworks
- Design and execution of computational experiments to study learning dynamics
- Development of theoretical models and mathematical analyses of learning processes
- Collaboration with team members on data analysis and interpretation
- Writing scientific publications for peer-reviewed journals
- Presentation of results at team meetings and scientific conferences

Compétences

## REQUIRED QUALIFICATIONS
**Education:**
- Master's degree (M2) or equivalent in Physics, Mathematics, Computer Science, Neuroscience, or related field

**Technical Skills:**
- Proficiency in Python programming and machine learning libraries (PyTorch, TensorFlow, JAX)
- Strong mathematical background: linear algebra, analysis, dynamical systems
- Knowledge of neural networks and deep learning principles
- Experience with numerical methods and scientific computing
- Analytical and problem-solving abilities

**Language Skills:**
- English: C1 level minimum (reading, writing, speaking)


## DESIRED QUALIFICATIONS
- Previous experience with trained neural networks
- Background in theoretical neuroscience or computational biology
- Knowledge of dynamical systems theory
- Experience with high-performance computing and cluster environments

Contexte de travail

The researcher will join the Group for Neural Theory at ENS, within a dynamic research environment. The team is part of the Laboratory of Cognitive and Computational Neuroscience (LNC2). This position is part of the ERC Synergy Chronology project, a collaboration with the experimental teams of Brice Bathellier (Institut Audition), Virginie van Wassenhove (NeuroSpin), and Mehrdad Jazayeri (MIT).

Contraintes et risques

No specific constraints.