Intern

About the Role

The Allen Institute accelerates science for a healthier world. The Neural Dynamics accelerator aims to understand how the brain generates flexible behavior by uncovering the algorithms implemented by neural circuits. We are seeking a graduate student intern to analyze large-scale neural and behavioral recordings to uncover how populations of neurons coordinate to control movement. This is a one-year, part-time research position at the intersection of machine learning and neuroscience.

Key Responsibilities

• Characterize how coordinated activity across two simultaneously recorded neural populations encodes forelimb force.
• Implement, fit, and evaluate machine learning and latent variable models that partition shared versus population-specific covariance, and benchmark them against recently published alternatives.
• Relate neural population activity to behavioral variables using regression and classification methods, with appropriate cross-validation and controls.
• Extend the analysis to electromyography (EMG) and synchronized behavioral video, and to how neural coding and behavior change across days of learning.
• Produce documented, version-controlled Python analysis code that other team members can build on.
• Communicate results to mentoring scientists and the wider team, and contribute figures and methods text toward posters and manuscripts.

Requirements & Tech Stack

• Scientific computing in Python (numpy, scipy, pandas).
• Python analysis code should be documented and version-controlled.

Qualifications

• Currently enrolled in a master’s or PhD program in electrical and computer engineering, computer science, or a related quantitative field.
• Completed coursework, prior to the start of the position, in programming, linear algebra, probability and statistics, and at least one of machine learning or signal processing.
• Available to start in late October / early November 2026 and able to commit to the position for one year.
• Able to work onsite at least one day per week.
• No prior neuroscience background is required.

Preferred Qualifications

• Coursework or project experience with linear regression and discrete classifiers (e.g., support vector machines).
• Coursework or project experience with dimensionality reduction and latent variable models (e.g., PCA, factor analysis, canonical correlation analysis).
• Experience with time series analysis and signal processing (filtering, spectral methods).
• Experience with scikit-learn and with deep learning frameworks such as PyTorch.
• Experience with video-based pose estimation tools (e.g., DeepLabCut, SLEAP, Lightning Pose) or with biosignal analysis such as EMG.
• Experience with software best practices (version control, code review, testing).
• Interest in computational or systems neuroscience; prior neuroscience coursework or research is welcome but not required.
• Strong written and verbal communication skills.
• Ability to work both independently and in a collaborative, multi-disciplinary environment.

Position Type/Expected Hours of Work

• During academic school year: part-time, up to 19 hours per week.
• During academic summer break: full-time, approximately 40 hours per week.
• Weekly hours are flexible and can be scheduled around academic coursework.
• One-year fixed-duration term position.
• Hybrid work environment (remote and onsite).
• Primary work location in Washington State.
• Able to work onsite at least one day per week.

Compensation

• $38.00 per hour (non-negotiable).

Additional Requirements

• A cover letter is required to be considered for this position.