École Normale Supérieure
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Postdoctoral Position in Cognitive Computational Neuroscience École Normale Supérieure in France
Degree Level
Postdoc
Field of study
Computer Science
Funding
Available
Deadline
Oct 31, 2026
Country
France
University
École Normale Supérieure
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About this position
A funded 12-month postdoctoral position is available at the École Normale Supérieure (ENS) in Paris in cognitive computational neuroscience, under the supervision of Valentin Wyart. The project is supported by the ANR project MONODEC and starts at the earliest in January 2027.
The postdoctoral researcher will investigate how motivation influences cognitive noise and improves human decision-making under uncertainty. The work includes testing the impact of cognitive noise and incentives across several decision-task variants and examining whether choices rely on shared neural representations. The project combines behavioural experiments, magnetoencephalography (MEG), computational modelling of behaviour, artificial neural networks, and neural decoding, including cross-condition generalisation and coding similarity analyses of MEG activity patterns.
The position is closely connected to a PhD project in the team on how abstract neural representations enable flexible human decision-making, and the postdoc will work closely with the PhD student on shared tasks, models, and analysis pipelines. Research will be carried out within ENS in Paris, with MEG recordings at the Paris Brain Institute (ICM) and access to the behavioural testing facilities of the Department of Cognitive Studies (DEC) of ENS.
Funding includes support for participation in international conferences, and the postdoctoral researcher may also contribute to the co-supervision of Master students. The post is full-time and temporary, with an application deadline of 31 October 2026.
Applicants should hold a PhD in cognitive neuroscience, computational neuroscience, cognitive science, experimental psychology, or a closely related discipline. Desired experience includes computational modelling of human behaviour, human neuroimaging ideally MEG or EEG, artificial neural networks, MATLAB and/or Python programming, and statistical analysis. Experience with decision-model fitting, neural decoding, peer-reviewed publications, and strong English communication skills are expected. Candidates must have defended their PhD before the start date.
To apply, send a CV, a cover letter outlining research interests, and contact details of two referees by email to [email protected].
Funding details
Available
What's required
Applicants should hold a PhD in cognitive neuroscience, computational neuroscience, cognitive science, experimental psychology, or a closely related discipline. They should have prior experience with computational modelling of human behaviour and/or human neuroimaging, ideally MEG or EEG, strong skills in artificial neural networks, strong programming skills in MATLAB and/or Python, and solid knowledge of statistical methods. Experience in designing, fitting, and comparing computational models of decision-making, or in multivariate analyses of neural data such as neural decoding, would be an advantage. A track record of publications in peer-reviewed journals, good communication skills in English, and the ability to work both independently and collaboratively are expected. Candidates must have defended their PhD before the start date in January 2027. A strong interest in human decision-making and cognitive variability is expected, and experience in behavioural experiments and MEG/EEG analysis is appreciated.
How to apply
Send a CV, cover letter describing your research interests, and contact details of two referees by email to Valentin Wyart. Use [email protected] for application submission.
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