Dear all, A two-year postdoctoral position is available at NeuroSpin/INM (Paris, France) under the supervision of Dr. Florent Meyniel. https://computationalbrainteam.com/en/recruitment/2-year-postdoctoral-postio... *Project* Computational and high field MRI characterization of learning and decision-making *Supervisor and contact* Dr Florent MEYNIEL https://www.unicog.org/lab/the-computational-brain/ *Duration and Dates* * Initial duration: two years * Extension possible * Full-time post * Preferred starting date: October 2026 to January 2027 *Project description* Learning and decision making are intertwined processes in many everyday situations. One example is when you decide where to have lunch: should you go to the nearby coffee shop or to the university cafeteria? Learning depends on choice, because you can learn which option you prefer by trying each option repeatedly, and decision making depends on learning, because you eventually want to select the option you have learned you like best. Uncertainty plays a key role in both learning1–4 and decision making5, especially when the environment is not stationary (e.g., a new brand now runs the nearby coffee shop and you like it less). In the ANR/CEA-funded BrainSync collaborative project, we are interested in characterizing the neural representations of uncertainty6–8 and value that emerge from learning and guide decisions. Our approach combines deep phenotyping approach and multimodal imaging in the EXPLORE task5. We collected data from 16 participants who participated in one behavioral session, two 7T fMRI sessions, and two MEG sessions. 56 other participants participated in one pupillometry session and one 3T fMRI session. We also plan to record a dataset on our 11.7T MRI, and we have started to collect intracranial EEG data. This large multimodal dataset allows us to estimate and test different computational models of the decision and learning processes. One postdoc is currently working on the MEG and iEEG data, and one PhD student is working on the behavioral and 11.7T data (to be collected). We are looking for another postdoc for the already collected fMRI datasets. The postdocs and PhD student will work together to perform analyses informed by all datasets. *Profile* Ph.D. in neuroscience, machine learning or psychology, with strong programming skills, ideally in Python. Previous experience with ideally fMRI, computational modeling. You will be responsible for analyzing fMRI data and disseminating results in internal seminars, international conferences and journal articles. The working language of the lab is English. French is not required. *Workplace and environment* Dr Florent MEYNIEL leads the Computational Brain team (more info here), which is located in two places. * Institute of NeuroModulation (INM), Sainte Anne Hospital, Paris, France. The INM is part of the GHU Paris, Psychiatry & Neurosciences. The INM combines clinical activities and innovative clinical research in psychiatry with basic research in computational neuroscience. Team members spend most of their days here. * NeuroSpin, Paris-Saclay Campus, France. NeuroSpin is part of the CEA (Commissariat à l'Energie Atomique). Directed by Prof. Stanislas DEHAENE, NeuroSpin is a world-class brain imaging center equipped with a MEG system (Elekta, Neuromag) and several human MRI scanners (3T Prisma, 7T and 11.7T), all for research purposes only. The community at NeuroSpin is very scientifically stimulating, combining MRI physicists, machine learning experts, and cognitive neuroscientists. Team members go there to collect data and collaborate with their colleagues in the Cognitive NeuroImaging Unit. *Application procedure* Please send an email to Florent Meyniel (florent.meyniel@cea.fr): * Your CV * a research statement describing your research interests and proposed directions * the contact details of two referees Applications will be considered on a rolling basis (positions will remain open until filled). *Salary * Will follow INM standards and depend on experience. *References* 1. Meyniel, F., Schlunegger, D. & Dehaene, S. The Sense of Confidence during Probabilistic Learning: A Normative Account. PLoS Comput Biol 11, e1004305 (2015). 2. Meyniel, F., Sigman, M. & Mainen, Z. F. Confidence as Bayesian Probability: From Neural Origins to Behavior. Neuron 88, 78–92 (2015). 3. Foucault, C. & Meyniel, F. Two Determinants of Dynamic Adaptive Learning for Magnitudes and Probabilities. Open Mind 8, 615–638 (2024). 4. Meyniel, F. Brain dynamics for confidence-weighted learning. PLOS Comput. Biol. 16, e1007935 (2020). 5. Paunov, A. et al. Multiple and subject-specific roles of uncertainty in reward-guided decision-making. 2024.03.27.587016 Preprint at https://doi.org/10.1101/2024.03.27.587016 (2024). 6. Walker, E. Y. et al. Studying the neural representations of uncertainty. Nat. Neurosci. 1–11 (2023) doi:10.1038/s41593-023-01444-y. 7. Bounmy, T., Eger, E. & Meyniel, F. A characterization of the neural representation of confidence during probabilistic learning. NeuroImage 268, 119849 (2023). 8. Meyniel, F. & Dehaene, S. Brain networks for confidence weighting and hierarchical inference during probabilistic learning. Proc. Natl. Acad. Sci. 201615773 (2017) doi:10.1073/pnas.1615773114. Best regards, Zaineb Amor, PhD Research Engineer GHU Paris, Psychiatrie et Neurosciences Institut de Neuromodulation Paris, France