Internship Openings

MERL is seeking a motivated graduate student to research methods for state and parameter estimation and optimization of large-scale systems for process applications. Representative applications include large vapor-compression cycles and other multiphysical systems for energy conversion that couple thermodynamic, fluid, and electrical domains. The ideal candidate would have a solid background in control and estimation, numerical methods, and optimization; strong programming skills and experience with Julia/Python/Matlab are also expected. Knowledge of the fundamental physics of thermofluid flows (e.g., thermodynamics, heat transfer, and fluid mechanics), nonlinear dynamics, or equation-oriented languages (Modelica, gPROMS) is a plus. The expected duration of this internship is 3 months.

MERL is looking for a highly motivated and qualified candidate to work on stochastic control for grid-interactive net-zero energy buildings informed by deep generative models. The ideal candidate will have a strong understanding of optimization-based control with expertise demonstrated via, e.g., publications, in stochastic model predictive control.

Additional understanding of energy systems and machine learning is a plus. Hands-on programming experience with numerical optimization solvers and Python fluency is required. The results of this 3-6 month internship are expected to be published in top-tier energy systems and/or control venues.

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 looking for a highly motivated and qualified candidate to work on estimation and calibration of muti-physical systems governed by differential algebraic equations (DAEs). The research will involve study, development and efficient implementation of estimation/calibration approaches for large-scale nonlinear systems, e.g., vapor compression cycles, with limited experimental data. The ideal candidate will have a strong background in one or multiple of the following topics: nonlinear control and estimation, optimization, and model calibration; with expertise demonstrated via, e.g., peer-reviewed publications. Prior experience in working with experimental data, and programming in Julia/Modelica is a plus. Senior PhD students in mechanical, electrical, chemical engineering or related fields are encouraged to apply. The typical duration of internship is 3 months, and the start date is flexible.

Required Specific Experience

• Graduate student with 2+ years of relevant research experience

Additional Desired Experience

• Strong programming skills in Julia or Modelica
• Prior experience in working with thermofluid systems
• Prior experience in estimation/calibration of complex nonlinear systems using experimental data

MERL is seeking a highly motivated and qualified intern to carry out research on mobile IoT network design methodology. The candidate is expected to develop innovative mobile network technologies to support UAV assisted IoT networks. The candidates should have knowledge of mobile network technologies such as path planning and cooperative network operations. Knowledge of UAV technology and mobility management is a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. Start date for this internship is flexible and the duration is 3 months.

The responsibilities of this intern position include (i) research on UAV assisted network design methodology; (ii) develop network configuration technologies to support UAV cooperative network operations; (iii) simulate and analyze the performance of developed technology.

MERL is seeking a highly motivated intern to research low-complexity (i.e., computationally efficient) formulations of model predictive control (MPC). Candidates should be currently enrolled in a PhD program and have theoretical background in MPC (e.g., an understanding of standard proofs of stability) and relevant concepts in convex optimization (e.g., an understanding of interior-point, active set, and first-order optimization methods). An ideal candidate would have prior research experience related to suboptimal MPC, real-time iterations strategies for MPC, and/or other low-complexity approximation methods for MPC, and convex optimization.

Publication of results produced during the internship is expected. The duration of the internships are 3-6 months, and the start dates are flexible.

Required Specific Experience

• Current enrollment in a PhD program in Aerospace, Mechanical, Electrical Engineering, or a related field
• Understanding of fundamental theoretical concepts in MPC (e.g., proofs of stability, recursive feasibility, etc.)
• Familiarity with optimization algorithms commonly used in MPC (e.g., interior-point, active set, and first-order methods)
• Strong programming skills in MATLAB, Python, and/or C/C++.

Additional Desired Experience

• Prior research experience related to suboptimal MPC and/or other low-complexity approximation methods for MPC.
• Prior research experience related to optimization algorithm development/analysis.

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 is seeking a graduate student to develop an optimization-based framework for nonconvex trajectory generation with emphasis on continuous-time modeling/constraint satisfaction, convergence guarantees, and real-time performance. The framework will support hybrid dynamical systems, spatio-temporal logical specifications, multi-body systems, and contact-rich motion. The methods will be evaluated on real-world robotics applications based on locomotion, manipulation, and motion planning. 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: sequential convex programming, augmented Lagrangian, operator-splitting first-order optimization algorithms, contact-rich motion, multi-body systems, signal temporal logic specifications, direct shooting and collocation methods.
• Experience in design and simulation tools for robotics such as ROS, Mujoco, Gazebo, Isaac Lab.
• Strong programming skills in Python and/or C/C++.

MERL is seeking interns passionate about robotics to contribute to the development of an Active Simultaneous Localization and Mapping (Active SLAM) framework guided by Large Language Models (LLM). The core objective is to achieve autonomous behavior for mobile robots. The methods will be implemented and evaluated in high performance simulators and (time-permitting) in actual robotic platforms, such as legged and wheeled robots. The expectation at the end of the internship is a publication at a top-tier robotic or computer vision conference and/or journal.

The internship has a flexible start date (Spring/Summer 2025), with a duration of 3-6 months depending on agreed scope and intermediate progress.

Required Specific Experience

• Current/Past Enrollment in a PhD Program in Computer Engineering, Computer Science, Electrical Engineering, Mechanical Engineering, or related field
• Experience with employing and fine-tuning LLM and/or Visual Language Models (VLM) for high-level context-aware planning and navigation
• 2+ years experience with 3D computer vision (e.g., point cloud, voxels, camera pose estimation) and mapping, filter-based methods (e.g., EKF), and in at least some of: motion planning algorithms, factor graphs, control, and optimization
• Excellent programming skills in Python and/or C/C++, with prior knowledge in ROS2 and high-fidelity simulators such as Gazebo, Isaac Lab, and/or Mujoco

Additional Desired Experience

• Prior experience with implementation and/or development of SLAM algorithms on robotic hardware, including acquisition, processing, and fusion of multimodal sensor data such as proprioceptive and exteroceptive sensors

MERL is seeking highly motivated interns for research positions in guidance, navigation, and control of spacecraft. The ideal candidates are PhD students with experience in one or more of the following topics: astrodynamics, the three-body problem, relative motion dynamics, station keeping, rendezvous, landing, attitude control, orbit control, orbit determination, nonlinear estimation, scheduling problems, and optimization-based control. Publication of results produced during the internship is expected. The duration of the internships are 3-6 months, and the start dates are flexible.

Required Specific Experience

• Current enrollment in a PhD program in Aerospace, Mechanical, Electrical Engineering, or a related field
• Strong programming skills in Matlab, Python, and/or C/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

MERL is seeking an outstanding intern to collaborate with the Control for Autonomy team in the development of trajectory generation for mobile robots, e.g., drones, equipped with controllable sensors, for information acquisition tasks. The project objective is to optimize drone trajectories and the control of on board sensors (e.g., field of view, pointing angle, etc.) to maximize the amount of information acquired about specified monitored targets while reducing the mission duration. The ideal candidate is expected to be working towards a PhD with a strong emphasis on trajectory generation and control, optimization-based control and planning algorithms and constrained control. Strong programming skills in at least one among Matlab, Python, Julia, C/C++ are required. Experience with experimental drone platforms such as crazyflie, and related software frameworks, such as ROS, are desired. The expected start date is in the late Spring/Early Summer 2025, for a duration of 3-6 months.

Required Specific Experience

• Currently enrolled in a PhD program in Aerospace, Electrical, Mechanical Engineering, Computer Science, Applied Math or a related field
• 2+ years of research in at least some of: optimization-based trajectory generation, convex and non-convex optimization, sensor modeling, information-aware planning
• Strong programming skills in at least one among Matlab, Python, Julia, or C/C++
• Validation of drone planning and control in simulations. Experience with drone experiments is a plus.

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 motivated and qualified individual to work on physics-informed scientific machine learning algorithms for problems governed by partial differential equations (PDEs). The ideal candidate will be a PhD student in engineering, computer science, or related fields with a solid background in scientific machine learning for PDEs. Preferred skills include knowledge of physics-informed neural networks, operator learning, nonlinear dimensionality reduction, and diffusion models. Strong coding abilities in Python and a popular deep learning framework such as Pytorch are essential. Publication of the results obtained during the internship is expected. The duration is expected to be at least 3 months with a flexible start date.

The Computational Sensing Team at MERL is seeking an intern to work with MERL researchers on algorithms based on interacting particle systems for solving inverse problems. The focus of the project is particle-efficiency and applicability to non-log-concave posterior distributions (which may result from nonlinear forward operators). The project includes algorithm design, (finite-particle) convergence analysis, and/or empirical evaluation for challenging inverse problems such as full waveform inversion. The ideal candidate would be a PhD student with a solid background in applied probability, nonconvex optimization, or Bayesian sampling. Programming skills in Python or MATLAB are required. The duration is anticipated to be at least 3 months with a flexible start date.

MERL is seeking a motivated and qualified individual to work on data-driven estimation and control of high-dimensional dynamical systems, with applications in indoor airflow optimization. The ideal candidate will be a PhD student in engineering or related fields with a solid background in estimation, control, and dynamical systems theory. Preferred skills include knowledge of reinforcement learning, data-driven control, nonlinear control, reduced-order modeling (ROM), and partial differential equations (PDEs). Publication of the results obtained during the internship is expected. The duration is expected to be at least 3 months with a flexible start date.

MERL is seeking a highly motivated and qualified intern to work on development of optimization algorithms for solving Mixed Integer Programs (MIPs). The ideal candidate would have significant research experience in theory and algorithms for solving MIPs including strong relaxations, cutting planes, and implementation of these techniques. Candidates at or beyond the middle of their Ph.D. program are encouraged to apply. The expected duration is for 3 months.

Required Specific Experience

• Experience with algorithms such as branch-and-price, column generation, benders.
• Familiarity with optimization software such as Gurobi, Cplex, SCIP.
• Proficiency in developing code in Python, C, C++.

MERL is looking for a highly motivated and qualified PhD student in the areas of motion planning, machine learning and control for robotics, to participate in research on advanced algorithms for motion planning and skill learning of robotic systems. Solid background and hands-on experience with classical motion planning and trajectory optimization algorithms for robotic manipulators is expected. Exposure to machine learning for policy optimization and skill learning, understanding of various optimization solvers and control theory is highly desirable. Familiarity with the use of machine learning algorithms for system identification of mechanical systems would be a plus, along with background in other areas of automatic control. Solid experimental skill and hands-on experience in coding in Python and ROS are required for the position. A successful internship will result in submission of results to top tier robotics venue in collaboration with MERL researchers. Start date is flexible, and the expected duration of the internship is 3-4 months. Interested candidates are encouraged to apply with their updated CV and list of publications.

Required Specific Experience

• Experience with robotic manipulators or other system like robot quadrupeds is required.
• Experience with motion planning and trajectory optimization algorithms
• Strong programming skills in Python and ROS
• Experience in at least one physics simulator

MERL is looking for a highly motivated individual to work on whole-body dexterous manipulation. The research will develop robot motor skills for whole-body, dexterous manipulation using optimization and/or learning algorithms. The ideal candidate should have experience in either one or multiple of the following topics: Optimization Algorithms for contact systems, Reinforcement Learning, control through contacts, and Behavioral cloning. Senior PhD students in robotics and engineering with a focus on contact-rich manipulation are encouraged to apply. Prior experience working with physical robotic systems (and vision and tactile sensors) is required as results need to be implemented on a physical hardware. Good coding skills in Python ML libraries like PyTorch etc. and/or relevant Optimization packages is required. A successful internship will result in submission of results to a peer-reviewed robotics journal in collaboration with MERL researchers. The expected duration of internship is 4-5 months with start date in May/June 2025. This internship is preferred to be onsite at MERL.

Required Specific Experience

• Prior experience working with physical hardware system is required.
• Prior publication experience in robotics venues like ICRA,RSS, CoRL.

MERL is seeking a motivated and qualified intern to conduct research on machine learning based electric motor design and optimization. Ideal candidates should be Ph.D. students with a solid background and publication record in electric machine design, optimization, and machine learning. Hands-on experience with the implementation of optimization algorithms, machine learning and deep learning methods is required. Strong programming skills using Python/PyTorch are expected. Knowledge and experience with electric machine principle, design and finite-element analysis are highly desirable. Start date for this internship is flexible and the duration is about 3 months.

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 is seeking a motivated and qualified intern to conduct research on shape and topology optimization of electrical machines. The ideal candidate should have a solid background and demonstrated research experience in mathematical optimization methods, including topology optimization, robust optimization, and sensitivity analysis, as well as machine learning methods. Hands-on coding experience with the implementation of topology optimization algorithms and finite-element simulation are desirable. Knowledge and experience with electric machine principle, design and finite-element analysis is a strong plus. Senior Ph.D. students in related expertise are encouraged to apply. Start date for this internship is flexible and the duration is around 3 months.

MERL is looking for a self-motivated intern to work on PWM inverter simulation and design. The ideal candidate would be a Ph.D. candidate in electrical engineering with solid research background in power electronics, control, and optimization. Experience in switching loss reduction modulation is desired. The intern is expected to collaborate with MERL researchers to carry out simulations, optimize design, analyze results, and prepare manuscripts for scientific publications. The total duration is 3 months.

Required Specific Experience

• Experience with simulation tools for PWM inverter design.