About the role#
This internship supports an investigation into autonomous operations and predictive maintenance for mechanical equipment. You will work on systems designed for future Lunar and Martian habitats, as well as smart-building management. The project uses the ACCEPT machine learning framework and hardware-in-the-loop testing in the SHARP Lab at NASA Ames. You will focus on edge-computing architecture and applied machine learning to improve how we monitor HVAC systems.
What you'll do#
- Develop and deploy Fault Detection and Diagnostics algorithms for HVAC systems.
- Integrate edge-compute environments with physical sensors.
- Apply machine learning and transfer learning techniques to Electrical Signature Analysis datasets.
- Predict mechanical degradation, such as compressor wear.
- Support research on autonomous operations and predictive maintenance.
What you'll need#
- Enrollment as an undergraduate (sophomore, junior, or senior) or graduate student (master's, doctoral, or post-doctoral).
- Proficiency in Python.
- Knowledge of edge-computing architectures.
- Experience with machine learning and transfer learning techniques.
- Majors in Mechanical, Civil, Computer, Electrical, or Industrial Engineering, Computer Science, or Mathematics.
- US citizenship.
Location & details#
- Location: Moffett Field, California.
- Modality: On-site.
- Term: Summer 2027 (June 7 to August 13).
- This is a full-time, unpaid internship.
About NASA - National Aeronautics and Space Administration
The National Aeronautics and Space Administration is a government agency founded in 1958. It conducts research and development in aeronautics, space exploration, and science. The organization operates from various locations across the United States, including its headquarters in Washington, DC. With over 53,000 employees, it manages missions involving rockets, spacecraft, and Earth observation.
How to get in at NASA - National Aeronautics and Space Administration
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