- Calabasas, California, United States
- Internship · Intern
- $51–$56 per hour
This internship focuses on machine learning surrogate models for engineering simulation and measurement. The intern will design, train, and evaluate neural surrogate models to predict engineering responses from simulation or measurement data faster than repeated high-fidelity simulation.
Compensation
(PhD) $51-$56
Responsibilities
- Generate parametric engineering datasets using simulation and/or measurement sources, with attention to sampling strategy and to numerical noise in the underlying data.
- Design, train, and evaluate neural surrogate models for engineering response prediction.
- Investigate how the choice of output representation affects accuracy, particularly across operating regions or frequency ranges where the response varies sharply.
- Establish rigorous baselines and quantify accuracy as a function of simulation or measurement budget.
- Evaluate LoRA and related parameter-efficient methods for specializing trained models to new problem settings, against full and partial fine-tuning.
Requirements
- PhD in Machine Learning
- Computer Science
- Applied Mathematics
- Electrical Engineering
- Electronic Engineering