Intern - Machine Learning for Neuroscience

A one-year machine-learning research internship with the Allen Institute Neural Dynamics accelerator. The intern will analyze recordings from two genetically distinct neural populations in the striatum alongside motor output, muscle activity and behavioral video. The project asks how shared and population-specific neural activity encodes movement, using latent-variable models, regression, classification and cross-validation. Work includes reproducible Python analysis and communicating results through figures and methods that may contribute to a poster or publication. No previous neuroscience background is required.

Start is expected in late October or early November 2026. The position pays $38 per hour, with up to 19 hours weekly during the academic year and approximately 40 during summer break. The hybrid role requires at least one on-site day weekly in Seattle, and any remote work must be in Washington State. University of Washington students may be able to arrange academic credit with their degree program and advisor.

Current enrollment in a master’s or PhD program in electrical/computer engineering, computer science or a related quantitative discipline. Prior coursework in programming, linear algebra, probability/statistics and machine learning or signal processing; scientific Python experience; availability for a one-year commitment starting late October/early November 2026; and at least one on-site day per week. A cover letter is required. Experience with latent-variable models, time-series analysis, PyTorch, pose estimation or EMG is preferred.