Internship - Scalable Numerical Solvers for Systems of Learned Dynamical Components

Graduate Student Internship MERL seeks a motivated graduate student to develop scalable numerical solvers for systems composed of learned dynamical components. The internship will focus on the numerical challenges that arise when multiple neural-network-based component models are interconnected through physical constraints and feedback, forming large-scale coupled ODE/DAE systems. The intern will develop numerical methods to improve solver robustness and simulation speed for these large-scale systems. 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. The internship is expected to last 3 months, with a flexible start date, and may be extended depending on research progress and mutual interest. The pay range for this internship position will be 6-8K per month. In addition to base pay, interns receive a relocation stipend, covered travel to and from MERL, and a monthly Charlie Card for local commuting. Interns are invited to participate in weekly social gatherings and 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. Employment is considered "at-will," and the Company reserves the right to modify base salary or any other compensation program at any time, including for reasons related to individual performance, departmental or Company performance, and market conditions.