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Data Science Engineering Summer 2026 Internship

Location: Remote (Global)
Time Commitment: ~20 hours/week, 3-4 months
Type: Unpaid Internship

Position Overview

We are seeking a highly motivated, self-directed Data Science Intern to join our research team. This role will focus on analyzing electrophysiology data from neurons and developing quantitative metrics to evaluate and compare signal processing performance between a ground-truth system and its reconstructed counterpart.

Key Responsibilities

  • Analyze and interpret electrophysiological data to identify patterns and insights
  • Design and implement metrics for comparing ground-truth neural signals with reconstructed data
  • Develop statistical pipelines for testing and validating neuronal circuit models
  • Build and maintain data pipelines for handling complex and large-scale datasets
  • Contribute to feature development and functionality based on evolving project needs
  • Document code and maintain high-quality technical documentation
  • Collaborate with researchers and technical volunteers
  • Participate in code reviews and assist with testing
  • Contribute to academic papers and potentially serve as a co-author

Qualifications

  • Currently pursuing a degree in Computer Science, Data Science, Statistics, Mathematics, Neuroscience, or related quantitative field
  • Strong proficiency in Python and experience with data science libraries (pandas, scikit-learn)
  • Experience working with version control tools such as Git
  • Demonstrated ability to analyze, manipulate, and model complex datasets
  • Understanding of statistical models and comfort working in data-rich environments
  • Excellent communication skills for presenting findings to both technical and non-technical audiences
  • Strong problem-solving skills and attention to detail
  • Interest in neuroscience, brain-machine interfaces, or whole brain emulation is highly desirable
  • Preferred: Strong background in neuroscience and experience analyzing neural signals

Apply Now

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