The Computational and Cognitive Neuroscience Lab (CCNL) at Dartmouth has openings for two enthusiastic graduate students to work on exciting projects on reward-based learning and decision making. One of the projects (funded by NIH) involves close collaboration with Alicia Izquierdo's lab at UCLA to investigate circuit-level neural mechanisms of adaptive learning. Starting date for one of the positions can be as early as Jan 2020 and the other position begins Sep 2020. A few recent relevant papers include: 1. Soltani A􏰀, Izquierdo A􏰀 (2019). Adaptive Learning under Expected and Unexpected Uncertainty. Nature Reviews Neuroscience <https://doi.org/10.1038/s41583-019-0180-y>. 2. Farashahi S, Donahue C, Hayden B, Lee D, Soltani A􏰀 (2019). Flexible Combination of Reward Information across Primates. Nature Human Behaviour <https://doi.org/10.1038/s41562-019-0714-3>. 3. Stolyarova A, Rakhshan M, Peters MA, Lau H, Soltani A􏰀, Izquierdo A􏰀 (2019). Contributions of Anterior Cingulate Cortex and Basolateral Amygdala to Learning and Choice under Perceptual Uncertainty. Nature Communications (accepted) [available on bioRxiv, 655860 <https://www.biorxiv.org/content/10.1101/655860v1>] For more recent publications from the lab please visit http://ccnl.dartmouth.edu <http://ccnl.dartmouth.edu/> Interested students please contact Alireza Soltani (soltani@dartmouth.edu).