Post-Doc position, ‘Motivation, Brain & Behavior’ lab, Paris Brain Institute, France

We are looking for a post-doctoral fellow to join our team (‘Motivation, Brain & Behavior’ lab) in the Paris Brain Institute. Any student with a PhD in a research field related to cognitive neuroscience is encouraged to apply. The position must be started between November 2021 and March 2022.

Who are we?

… a cognitive neuroscience research team led by Mathias Pessiglione, Jean Daunizeau and Sebastien Bouret (see website https://sites.google.com/site/motivationbrainbehavior/).

Our scientific questions revolve around how the brain makes decisions, why choices are irrational and whether pathological decisions can explain neuropsychiatric disorders. 

Our main approaches combine electrophysiology, functional neuroimaging (MRI), computational modeling and clinical investigations. 

We are based in the Paris Brain Institute, the largest neuroscience research center in France (see website https://institutducerveau-icm.org/en/).

What is the job?

… to conduct a funded project aiming to better understand, detect and treat mental fatigue. It will be supervised by Mathias Pessiglione and paid by the French National Research Agency, for a duration of 18 months (renewable for 12 months).

On a basic level, the idea is that fluctuations in brain metabolites condition the ability to exert cognitive control and therefore to overcome bias in decision making. 

On a clinical level, the objective is to provide computational explanations and predictive markers of mental fatigue in neurology (patients with glioma) and psychiatry (patients with depression).

See related previous publication (Blain et al. PNAS 2016)

https://www.pnas.org/content/113/25/6967.short

How do you get it?

… by sending a CV to mathias.pessiglione@gmail.com, with brief narrative of previous achievements, research interests and career goals, plus 2-3 names for references (or recommendation letters).

Expertise in behavioral testing, MRI spectroscopy, scalp EEG and/or computational modeling would be appreciated.