CATNIP Lab is looking for awesome people: postdocs, graduate & undergraduate students interested in computational neuroscience and machine learning.
For further information please contact I. Memming Park.
A full-time postdoctoral position is available in the Computational And Theoretical Neural Information Processing lab at Stony Brook University. 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.
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.
Strong background in mathematics, statistics, physics, computer sceince, or electrical/biomedical engineering is highly encouraged.
There are several neural data analysis projects as well as pure machine learning projects.
Neurobiology students who are working with complex high-dimensional data are welcome to bring their data. We specialize in spike train analysis, but other time series modalities are also welcome.
Undergraduate students who are interested in analyzing and visualizing neural data are welcome to inquire.
Prior experience on numerical computing (MATLAB, python (numpy/theano), julia, R) is strongly encouraged.