Optimization

MERL is seeking a graduate student to develop a scalable optimization-based framework for feedforward-feedback co-design for nonlinear dynamical systems subject to path constraints. The framework will 1) support modeling and operational uncertainties, and 2) guarantee static and dynamic feasibility in closed-loop. The solution approach will leverage the state-of-the-art in sequential convex programming, contraction analysis, and first-order methods for semidefinite programming. The methods will be evaluated on high-dimensional motion planning problems in robotics. The results of the internship are expected to be published in top-tier conferences and/or journal in robotics, control systems, and optimization.

The internship is expected to start in Spring or Summer 2025 with an expected duration of 3-6 months depending on the agreed scope and intermediate progress.

Required Specific Experience

• Current/Past enrollment in a Ph.D. program in Mechanical, Aerospace, Electrical Engineering, Computer Science, or Applied Mathematics.
• 2+ years of research in at least some of: first-order algorithms for SDPs, contraction analysis, nonconvex trajectory optimization.
• Strong programming skills in Python and/or C/C++.

MERL is seeking a highly motivated and qualified individual to conduct research in safe/robust whole-body motion planning and control of mobile manipulators. The ideal candidate should demonstrate solid background and track record of publications in the areas of robotic dynamics, motion planning, and control. Strong C++ and Python coding skills, knowledge of robotic software such as Pinocchio/Pybullet/MuJoCo, and optimization tools such as CasADi/PyTorch are a necessity. Ph.D. students in mechanical engineering, robotics, computer science, and electrical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

Required Specific Experience

• Solid background and track record of conducting innovative research in the dynamic modeling, motion planning, and control of robotic systems.
• Experience with C++/Python, Pinocchio, Pybullet, MuJoCo, CasADi, PyTorch.

MERL seeks a qualified, highly-motivated graduate student for an internship in the area of systems-level dynamic modeling, analysis and optimal control of next-generation thermofluid systems used in heating, cooling and ventilation (HVAC) applications. HVAC systems for applications such as data centers or district heating and cooling are characterized as dynamic networks, described by a large sets of differential and algebraic equations expressing physics (conservation laws), together with discrete and continuous equations describing the action of control. These are large scale, hybrid, constrained nonlinear systems. The MS group at MERL invites qualified graduate students to join its efforts in system level dynamic modeling, analysis and especially control of these systems. The research results are expected to impact both development of new products at Mitsubishi Electric, and also be published in leading conferences and journals.

Required Specific Experience

• Strong education and experience with nonlinear differential-algebraic equations is required.
• Strong education and working knowledge of optimal and nonlinear control theory is required.
• Knowledge of mathematical methods for hybrid systems is an asset.
• Some experience with thermofluid systems is an asset.