Internship - Scalable Numerical Solvers for Systems of Learned Dynamical Components

Mitsubishi Electric Research Laboratories is seeking a graduate student intern to develop scalable numerical solvers for systems of learned dynamical components. The role focuses on improving solver robustness and simulation speed for large-scale coupled ODE/DAE systems formed by interconnected neural-network-based models, physical constraints, and feedback.

What you'll do:

• Develop scalable numerical solvers for systems composed of learned dynamical components
• Develop numerical methods to improve solver robustness and simulation speed for these large-scale systems

What they're looking for:

• Candidates should have a strong foundation in numerical analysis and scientific computing, together with a solid background in machine learning and data-driven methods
• Strong programming skills in Python, Julia, C/C++, or Matlab are expected
• Senior PhD students in applied mathematics, scientific computing, computational engineering, or related fields are encouraged to apply

Benefits:

• MERL provides immigration support for qualified candidates as needed.
• In addition to base pay, interns receive a relocation stipend
• Covered travel to and from MERL
• A monthly Charlie Card for local commuting
• Weekly social gatherings
• Professional development opportunities, including research talks by both internal and external speakers
• Interns who meet the 90-day waiting period are also eligible for health insurance coverage
• MERL provides immigration support for qualified candidates as needed