A full-time postdoctoral position is available in the Computational and Theoretical Neural Information Processing Lab <http://catniplab.github.io/> at Stony Brook University. We design statistical models and machine learning methods specialized for neural data. We aim to understand how information and computations are represented and implemented in the brain, both at a single-neuron and systems level. We collaborate with experimental labs on important problems in neuroscience, such as sensory coding and perceptual decision-making. Current projects include inferring the latent dynamics of a neural population in awake behaving monkeys using recorded spikes and local field potential, and building scalable statistical models for high-dimensional neural observations. Our lab provides a friendly and highly collaborative environment. Requirements: Candidate must have a PhD or equivalent in neuroscience, statistics, engineering, mathematics or a related field. Ideal candidate would be familiar with machine learning and/or neural modeling. Prior experience in analyzing neural data, high-dimensional data, and/or non-Gaussian time series is a plus but not required. Good numerical programming skills and experience with professional software development are expected. Apply via https://stonybrooku.taleo.net/careersection/2/jobdetail.ftl?job=1500497 For further information please contact I. Memming Park ( memming.park@stonybrook.edu). Sincerely, Memming