MERL seeks a motivated graduate student to develop scalable numerical solvers for systems of learned dynamical components. The internship focuses on 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.
#J-18808-LjbffrIntern: Scalable Solvers for Learned Dynamical Systems in cambridge at Unknown Company
This position is listed as internship and onsite.