Role Overview
We are seeking a highly motivated and detail-oriented Electrical Engineering PhD (or strong MS) intern to join our Electrical Machines / Drive Unit Engineering team for Summer 2026. In this role, you will support experimental characterization of electrical machines (e.g., loss measurements, efficiency mapping, thermal/operational behavior) and perform data post-processing and signal analysis using MATLAB/Simulink and Python. If time allows, you will also explore surrogate modeling and uncertainty quantification (UQ) using statistical and machine-learning methods to accelerate machine characterization and insight generation.
This internship is ideal for someone who enjoys hands-on lab work, rigorous data analysis, and building practical engineering tools that improve test throughput and model fidelity.
You Will:
- Plan and execute electrical machine characterization tests, including torque‑speed and efficiency mapping
- Conduct loss and thermal measurements across operating conditions
- Support instrumentation and data acquisition, working with power analyzers, torque sensors, speed sensors, thermocouples/RTDs, and DAQ systems
- Ensure data quality through calibration checks, repeatability validation, filtering, and documentation
- Develop MATLAB/Simulink and Python scripts for signal processing, data cleaning, visualization, and reporting
- Extract key KPIs such as efficiency, loss breakdowns, harmonics/THD, and thermal steady‑state behavior
- Assist with surrogate modeling, uncertainty quantification, and ML‑based analytics for performance and loss prediction
- Gain experience performing high‑quality electrical machine testing and translating data into engineering insights
- Opportunity to build MATLAB and Python toolchains used in production engineering workflows
- Exposure to advanced modeling techniques, including surrogate models and uncertainty quantification
You Bring:
- Strong background in electrical engineering, with focus on electrical machines and drives
- Hands‑on experience with electrical machine testing or experimental lab work
- Proficiency in MATLAB/Simulink and Python for data analysis and automation
- Familiarity with one or more of the following:
- Efficiency mapping and loss segregation (copper, iron, mechanical losses)
- Frequency‑domain analysis (FFT, harmonic analysis) and sensor calibration
- Statistical methods and basic uncertainty estimation
- Interest in surrogate modeling, UQ, or ML‑enabled engineering workflows
At Lucid, we don’t just welcome diversity - we celebrate it! Lucid Motors is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, national or ethnic origin, age, religion, disability, sexual orientation, gender, gender identity and expression, marital status, and any other characteristic protected under applicable State or Federal laws and regulations.
