Internship Openings

MERL is seeking a highly motivated and qualified individual to conduct research in the integration of model- and learning-based control to achieve high precision positioning with guaranteed safety and robustness. The ideal candidate should have solid backgrounds in dynamical systems, control theory and state-of-the-art control policy learning algorithms, and strong coding skills. Prior experience on ultra-high precision motion control systems is a plus. Ph.D. students in learning and control are encouraged to apply. 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 differentiable modeling, estimation and control of electrical machines. The ideal candidate should have solid backgrounds in dynamical modeling of electrical machines, parameter estimation, and control theory. A proven record of publishing results in leading conferences/journals is necessary. Demonstrated knowledge of sensorless drive and experience of using dSPACE for real-time HIL experimentation is a plus. Senior Ph.D. students in electrical engineering, control, and related areas are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

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 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 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 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 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 highly skilled and self-motivated intern to work on motion planning of articulated vehicles. The ideal candidate should have solid backgrounds in established path/motion planning algorithms (A*, D*, graph-search) and optimization-based control for ground and articulated vehicles. Excellent coding skills in MATLAB/Simulink and publication records are necessary. Experience with CasADi and dSPACE is a plus. Ph.D. students in robotics, computer science, control, electrical engineering, or related areas are encouraged to apply. Start date for this internship is flexible, and the expected duration is about 4-6 months.

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 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 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.

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 motivated and qualified individual to work on fast surrogate models for sound emission and propagation from complex vibrating structures, with applications in HVAC noise reduction. The ideal candidate will be a PhD student in engineering or related fields with a solid background in frequency-domain acoustic modeling and numerical techniques for partial differential equations (PDEs). Preferred skills include knowledge of the boundary element method (BEM), data-driven modeling, and physics-informed machine learning. 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.