Machine Learning Engineering Summer 2026 Unpaid Internship

IMPORTANT: Please apply through our Google Form .

Your application may not be seen if you apply via LinkedIn or Idealist. Please apply using our Google Form.

LinkedIn Applicants - Please copy and paste the link below to fill out the Google Form:https://docs.google.com/forms/d/e/1FAIpQLSckOacnYFLSx5SnWfRUDAstVaUBtokfcgMrtHmtGmf_EOHF2Q/viewform?usp=publish-editor

If your qualifications are a strong match, we’ll be in touch about the next steps.

About Carboncopies Foundation

The Carboncopies Foundation is an international nonprofit organization dedicated to advancing the science and technology of Whole Brain Emulation (WBE). Our mission is to map and functionally recreate neural architectures, moving human cognition from its biological substrate to computational neural prostheses. For over 15 years, we have served as a global nexus for neuroscience, nanotechnology, and computer science. Our work represents a profound scientific challenge and a potential pathway to extending human experience and consciousness beyond current biological limitations.

This is a REMOTE UNPAID Summer Intern position*

Position Overview

We are seeking a Machine Learning Engineering Intern to join our research team for Summer 2026. This role focuses on the practical application of classical machine learning and statistical modeling to solve problems in neural data analysis and system identification. Unlike deep learning research roles, this position prioritizes "traditional" ML approaches, feature engineering, and robust algorithmic implementation to ensure the reliability and interpretability of our neural models.

You will work closely with Data Scientists and Neuroscientists to automate data processing and develop predictive models that help bridge the gap between biological observations and computational simulations.

Key Responsibilities

• Algorithm Implementation: Deploy and tune classical ML models (e.g., Random Forests, Gradient Boosting, SVMs) to classify neural states and predict signal trends.
• Feature Engineering: Extract meaningful features from high-dimensional electrophysiological data to improve model performance and interpretability.
• Data Preprocessing: Develop automated pipelines to clean and normalize large-scale datasets, handling noise inherent in biological recordings.
• Model Evaluation: Perform rigorous cross-validation and error analysis to ensure models are generalizable across different neural circuit datasets.
• Clustering & Dimensionality Reduction: Use unsupervised techniques (e.g., PCA, K-means) to identify patterns in neural firing populations.
• Documentation: Maintain high-quality code to ensure that ML workflows can be integrated into the Foundation’s long-term WBE roadmap.

Qualifications

• Academic Standing: Currently pursuing a degree in Computer Science, Data Science, Statistics, or a related quantitative field.
• Core Tech Stack: Strong proficiency in Python and standard ML libraries (e.g., scikit-learn, NumPy, pandas).
• Statistical Fundamentals: A solid understanding of probability, statistics, and the mathematical foundations of machine learning.
• Data Fluency: Experience working with time-series data or large structured datasets.
• Problem Solving: Ability to take a high-level research question and translate it into a structured ML task.
• Communication: Capable of explaining model choices and performance metrics to a multidisciplinary team.

Time Commitment

• Flexible: Approximately 20 hours per week.
• Internship timeframe: 5-6 months.

Benefits

• Pioneering Research: Gain hands-on experience in the field of neurotechnology and the quest for Whole Brain Emulation.
• Professional Mentorship: Work alongside senior researchers in a collaborative, remote-first environment.
• Academic Recognition: Opportunity to contribute to internal technical reports and potential co-authorship on papers.
• Global Network: Connect with a world-class community of neuroscientists and engineers.

How to Apply If you are passionate about applying machine learning to some of the most profound questions in science, we want to hear from you. Please submit your application via Google Form. Be sure to include a link to your GitHub or a portfolio highlighting your practical ML projects.

LinkedIn Applicants - Please copy and paste the link below to fill out the Google Form:https://docs.google.com/forms/d/e/1FAIpQLSckOacnYFLSx5SnWfRUDAstVaUBtokfcgMrtHmtGmf_EOHF2Q/viewform?usp=publish-editor

If your qualifications are a strong match, we’ll be in touch about the next steps.

Note: The Carboncopies Foundation is an equal opportunity organization. We welcome applicants from all backgrounds who are dedicated to the ethical and scientific advancement of WBE.

Website: https://carboncopies.org/

LinkedIn: https://www.linkedin.com/company/carbon-copies

Instagram: https://www.instagram.com/carboncopies_foundation/

Facebook: https://www.facebook.com/groups/carboncopies/

Twitter: https://twitter.com/carboncopiesorg

YouTube: https://www.youtube.com/channel/UCuNZLgW-6Xcp6wfyb2Y_Thw