ML Neuroscience Intern - Hybrid: Neural Data Modeling

ClearCompany Talent Management Software; Seattle (WA); Hybrid; USD 36.000 - 41.000; Teilzeit

Zusammenfassung

The Allen Institute is seeking a Research Intern in Machine Learning for Neuroscience in Seattle, WA. Work with neural and behavioral data to understand multi-population encoding of movement, applying ML, latent variable models, and reproducible Python workflows.

Part-time, one-year term with hybrid onsite/remote options. Mentored by staff scientists; potential for conference or manuscript contributions; UW affiliation possible for academic credit.

Qualifikationen

• Enrolled in master's or PhD in a quantitative field with funding or status to engage in research.
• Strong background in Python-based scientific computing and ML/signal processing.
• Experience with numpy, scipy and pandas for data analysis.

Aufgaben

• Characterize coordinated neural activity encoding forelimb force.
• Implement and evaluate ML and latent-variable models to partition covariance.
• Relate neural activity to behavior via regression and classification with CV.
• Extend analyses to EMG and video, tracking learning across days.
• Produce documented Python code and contribute to posters/manuscripts.
• Communicate results to mentoring scientists and team members.

Kenntnisse

Python

Numpy

Scipy

Pandas

Machine Learning

Signal Processing

Scikit-learn

PyTorch

DeepLabCut

SLEAP

Lightning Pose

Ausbildung

Master's or PhD in electrical and computer engineering, computer science, or related quantitative field

Coursework in programming, linear algebra, probability and statistics, ML or signal processing

Python scientific computing (numpy, scipy, pandas)

Tools

Python

DeepLabCut

SLEAP

Lightning Pose

Jobbeschreibung

The Allen Institute is seeking a Research Intern in Machine Learning for Neuroscience in Seattle, WA. Work with neural and behavioral data to understand multi-population encoding of movement, applying ML, latent variable models, and reproducible Python workflows.

Part-time, one-year term with hybrid onsite/remote options. Mentored by staff scientists; potential for conference or manuscript contributions; UW affiliation possible for academic credit.