Postdoc in Computational Neuroscience, Cambridge-Columbia collaboration We are seeking a highly creative and motivated postdoctoral fellow to work on a collaborative project between the labs of Guillaume Hennequin and Máté Lengyel at the Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, and Daniel Wolpert at the Zuckerman Mind Brain Behavior Institute, Columbia University. The project studies neural network mechanisms underlying the context-dependent (continual) learning of motor repertoires and ties together several threads of research recently developed in our labs, based on the following key publications (note that several are collaborative between our groups): - JB Heald, M Lengyel, DM Wolpert (2021) Contextual inference underlies the learning of sensorimotor repertoires. Nature 600:489-493. - TC Kao, MS Sadabadi, G Hennequin (2021). Optimal anticipatory control as a theory of motor preparation: a thalamo-cortical circuit model. Neuron 109:1567-1581. - TC Kao, KT Jensen, GM van de Ven, A Bernacchia, G Hennequin (2021) Natural continual learning: success is a journey, not (just) a destination. NeurIPS, https://tinyurl.com/2jfyss8c - Echeveste R, Aitchison L, Hennequin G, Lengyel M (2020) Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference. Nature Neuroscience 23:1138-1149. The postdoc will be based in Cambridge, with an opportunity for regular visits to Columbia. The successful candidate will have - a strong quantitative background - demonstrable interest in theoretical neuroscience - obtained (or be close to the completion of) a PhD or equivalent in computational neuroscience, physics, mathematics, computer science, machine learning or a related field Preference will be given to candidates with - previous experience in computational neuroscience, especially with the dynamics of recurrent neural network, and function-optimized neural networks - sufficient programming skills to run numerical simulations and to use large scale optimization packages - expertise with advanced data analysis and Bayesian techniques For further details and to send your application, please go to https://www.jobs.cam.ac.uk/job/33691/. For informal queries, please contact Guillaume Hennequin <g.hennequin@eng.cam.ac.uk>, Máté Lengyel <m.lengyel@eng.cam.ac.uk> or Daniel Wolpert <wolpert@columbia.edu>. Máté Lengyel -- Professor of Computational Neuroscience Computational and Biological Learning Lab Cambridge University Engineering Department Trumpington Street, Cambridge CB2 1PZ, UK tel: +44 (0)1223 748 532, fax: +44 (0)1223 765 587 email: m.lengyel@eng.cam.ac.uk web: lengyellab.org