Control Systems / Motor Control Engineering Intern

Job Type

Internship

Description

Quantic BEI | Aerospace & Defense Technology

Quantic BEI is seeking a highly motivated Control Systems Engineering Intern to support development of precision electromechanical systems operating in extreme environments. This role focuses on control theory, motor control, and advanced embedded implementation for mission-critical aerospace and defense applications.

We are especially interested in candidates pursuing or currently enrolled in a Master’s or PhD program with strong controls background.

What You’ll Do

• Support firmware development for real-time embedded motor control
• Develop and implement motor control algorithms (FOC, PID, state-space, etc.)
• Model electromechanical systems and design control loops
• Assist with hardware bring-up, tuning, and performance optimization
• Analyze system stability, bandwidth, noise, and disturbance rejection
• Conduct lab testing using oscilloscopes, current probes, DAQ systems, etc.
• Document models, control strategies, and test results
  
  

What You’ll Gain

• Hands-on experience developing precision control systems for aerospace and defense
• Exposure to high-performance electromechanical system design
• Mentorship from experienced controls and embedded engineers
• Opportunity to work on mission-critical hardware
• Potential pathway to full-time opportunities
  
  

If you’re passionate about applying advanced control theory to real hardware, we’d love to connect.

Requirements

What We’re Looking For

• Currently pursuing a MS or PhD (preferred) in Electrical Engineering, Controls, Robotics, or related field
• Strong foundation in control theory
• Hands-on experience with motor control (BLDC, PMSM, stepper, servo, etc.)
• Experience implementing control algorithms in C/C or similar
• Comfort with MATLAB/Simulink or similar modeling tools
• Strong analytical and problem-solving skills
  
  

Bonus Experience

• Field-Oriented Control (FOC) implementation
• Real-time systems or RTOS experience
• Experience with STM32 or similar motor-control platforms
• State estimation (Kalman filters, observers)
• System identification and frequency-domain analysis
• FPGA-based control implementation