PhD position in cognitive computational neuroscience

Ecole Normale Supérieure; France; Sur place; EUR 24 000 - 30 000; Plein temps

Résumé du poste

École Normale Supérieure in Paris invites applications for a funded 36-month PhD position under the supervision of Valentin Wyart. The project studies how abstract neural representations support flexible human decision-making, combining behavioural tasks, MEG, and computational modelling to explore how the brain generalises across perceptual and reward-guided decisions.

The selected candidate will receive training in cognitive and computational neuroscience, with MEG recordings and collaboration

Qualifications

• Master’s degree or equivalent in a relevant field (cognitive science, neuroscience).
• Strong quantitative and programming skills (MATLAB and/or Python) and knowledge of statistical methods.
• Experience with behavioural experiments, MEG/EEG, computational modelling, or multivariate data analysis is advantageous.

Connaissances

Quantitative skills

Programming skills

Behavioural experiments

MEG/EEG

Computational modelling

Multivariate data analysis

English communication

Formation

Master's degree in cognitive science/neuroscience

Outils

MATLAB

Python

Description du poste

Organisation/Company Ecole Normale Supérieure Research Field Neurosciences » Neuropsychology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 11 Oct 2026 - 23:59 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Dec 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

A funded 36-month PhD position is available at the École Normale Supérieure (ENS) in Paris, under the supervision of Valentin Wyart. The project investigates how abstract neural representations enable flexible human decision-making. The PhD student will combine behavioural experiments, magnetoencephalography (MEG), computational modelling of behaviour, and neural decoding to study how the brain combines multiple sensory features, corrects biased sensory information, and generalises computations across perceptual and reward-guided decisions. The position offers training in cognitive and computational neuroscience, with MEG recordings and collaboration with researchers specialising in recurrent neural network modelling.

Requirements

Research Field Neurosciences Education Level Master Degree or equivalent

Skills/Qualifications

Applicants should hold a Master’s degree in cognitive science, neuroscience, or a closely related discipline. They should have strong quantitative and programming skills (MATLAB and/or Python), knowledge of statistical methods, and a keen interest in the neural and computational mechanisms of human decision-making. Experience with behavioural experiments, MEG/EEG, computational modelling, or multivariate data analysis would be an advantage. Good communication skills in English and the ability to work both independently and collaboratively are expected. Training in the project’s experimental and analytical methods will be provided.

Specific Requirements

A strong interest in human decision-making, quantitative data analysis, and experimental research is expected. Prior experience with MEG/EEG, neural decoding, or computational modelling would be an advantage but is not essential, as training will be provided.