<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">2-YEAR POSTDOCTORAL POSITION AVAILABLE AT NEUROSPIN/INM (PARIS, FRANCE)</div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class=""><a href="https://florentmeyniel.weebly.com/uploads/5/9/7/2/59727215/ad_recruitment_postdoc.pdf" class="">https://florentmeyniel.weebly.com/uploads/5/9/7/2/59727215/ad_recruitment_postdoc.pdf</a></div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 14px;" class=""><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class=""><b class="">Project</b></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">Deep phenotyping of learning and decision-making 7T MRI, MEG & computational modeling</div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 14px;" class=""><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class=""><b class="">Supervisor and contact</b></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">Dr Florent MEYNIEL</div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class=""><a href="https://www.unicog.org/lab/the-computational-brain/" class="">https://www.unicog.org/lab/the-computational-brain/</a></div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 14px;" class=""><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class=""><b class="">Duration and Dates</b></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">* Initial duration: two years</div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">* Extension possible</div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">* Full-time post</div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">* Preferred starting date: January 2025</div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 14px;" class=""><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class=""><b class="">Project description</b></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">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). </div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 14px;" class=""><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">In the CEA-funded EXPLORE+ collaborative project, we are interested in characterizing the neural representation of uncertainty6–8 and value that emerge from learning and guide decisions. Our approach follows a deep phenotyping approach, attempting to characterize each subject with a large multimodal dataset. We collected data from 16 participants who participated in one behavioral session, two 7T fMRI sessions, and two MEG sessions. The large number of trials allows us to estimate and test different computational models of the decision and learning processes. The 7T MRI and MEG data provide access to the topographical organization of neural representations and their dynamics, respectively, to better understand learning and decision making. </div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 14px;" class=""><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">One postdoc is currently working on the fMRI data, and we are looking for another postdoc for the MEG dataset. Both postdocs will work together to perform analyses informed by both modalities.</div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">The EXPLORE+ project will continue with another previously funded project called BrainSync, which will collect data from 11.7 fMRI and intracranial recordings using the same task, providing an opportunity to extend the current work.</div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 14px;" class=""><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class=""><b class="">Profile</b></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">Ph.D. in neuroscience, machine learning or psychology, with good programming skills (ideally Python). Previous experience with ideally MEG, EEG or alternatively fMRI, computational modeling. You will be responsible for data analysis (mainly MEG, also fMRI-MEG in collaboration with Alexander Paunov, postdoc working on the fMRI part) and dissemination of results in internal seminars, international conferences and journal articles.</div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 14px;" class=""><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">The working language of the lab is English. French is not required.</div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 14px;" class=""><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class=""><b class="">Workplace and environment</b><span class="Apple-tab-span" style="white-space:pre"> </span></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">Dr Florent MEYNIEL leads the Computational Brain team (more info here), which is located in two places.</div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">* 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.</div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 14px;" class=""><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">* 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 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. </div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 14px;" class=""><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class=""><b class="">Application procedure</b></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">Please send an email to Florent Meyniel (<a href="mailto:florent.meyniel@cea.fr" class="">florent.meyniel@cea.fr</a>) and Alexander Paunov (<a href="mailto:alexander.paunov@gmail.com" class="">alexander.paunov@gmail.com</a>):</div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">* Your CV</div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">* a research statement (what you like and want to do)</div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">* the contact details of two referees</div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 14px;" class=""><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">Applications will be considered on a rolling basis (positions will remain open until filled).</div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 14px;" class=""><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class=""><b class="">Salary</b></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">According to CEA standards. According to experience.</div><div style="margin: 0px; font-stretch: normal; line-height: normal; min-height: 14px;" class=""><br class=""></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class=""><b class="">References</b></div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">1. Meyniel, F., Schlunegger, D. & Dehaene, S. The Sense of Confidence during Probabilistic Learning: A Normative Account. PLoS Comput Biol 11, e1004305 (2015).</div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">2. Meyniel, F., Sigman, M. & Mainen, Z. F. Confidence as Bayesian Probability: From Neural Origins to Behavior. Neuron 88, 78–92 (2015).</div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">3. Foucault, C. & Meyniel, F. Two Determinants of Dynamic Adaptive Learning for Magnitudes and Probabilities. Open Mind 8, 615–638 (2024).</div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">4. Meyniel, F. Brain dynamics for confidence-weighted learning. PLOS Comput. Biol. 16, e1007935 (2020).</div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">5. Paunov, A. et al. Multiple and subject-specific roles of uncertainty in reward-guided decision-making. 2024.03.27.587016 Preprint at <a href="https://doi.org/10.1101/2024.03.27.587016" class="">https://doi.org/10.1101/2024.03.27.587016</a> (2024).</div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">6. Walker, E. Y. et al. Studying the neural representations of uncertainty. Nat. Neurosci. 1–11 (2023) doi:10.1038/s41593-023-01444-y.</div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">7. Bounmy, T., Eger, E. & Meyniel, F. A characterization of the neural representation of confidence during probabilistic learning. NeuroImage 268, 119849 (2023).</div><div style="margin: 0px; font-stretch: normal; line-height: normal;" class="">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.</div></body></html>