Control

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.

MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in the development of visual perception-aware control. The overall objective is to optimize control policy where the perception uncertainty is affected by the chosen policy. Application areas include mobile robotics, drones, autonomous vehicles, and spacecraft. The ideal candidate is expected to be working towards a PhD with a strong emphasis on stochastic optimal control/planning or visual odometry and to have interest and background in as many as possible among: output-feedback optimal control, visual SLAM, POMDP, information fields, motion planning, and machine learning. The expected start date is in the late Spring/Early Summer 2025, for a duration of 3-6 months.

Required Specific Experience

• Current/Past enrollment in a PhD program in Mechanical, Aerospace, Electrical Engineering, or a related field
• 2+ years of research in at least some of: optimal control, motion planning, computer vision, navigation, uncertainty quantification, stochastic planning/control
• Strong programming skills in Python and/or C++

MERL seeks graduate students passionate about robotics to collaborate and develop a framework for infrastructure monitoring using quadrotors. The work will involve multi-domain research, including multi-agent planning and control, SLAM, and perception. The methods will be implemented and evaluated on an actual robotic platform (Crazyflies). The results of the internship are expected to be published in top-tier conferences and/or journals. The internship will take place during summer 2025 (exact dates are flexible) with an expected duration of 3-4 months.

Please use your cover letter to explain how you meet the following requirements, preferably with links to papers, code repositories, etc., indicating your proficiency.

Required Specific Experience

• Current enrollment in a PhD program in Mechanical, Electrical Engineering, Computer Science, or related programs, with a focus on Robotics and/or Control Systems
• Experience in some/all of these topics: multi-agent motion planning, constrained control, SLAM, computer vision
• Experience with ROS2 and validation of algorithms on robotic platforms, preferably quadrotors
• Strong programming skills in Python and/or C/C++

Desired Specific Experience

• Experience with Crazyflie quadrotors and the Crazyswarm library
• Experience with the SLAM toolbox in ROS2
• Experience in convex optimization and model predictive control
• Experience with computer vision