Internship Openings

MERL seeks graduate students passionate about robotics to contribute to the development of a framework for legged robots with manipulator arms to collaborate with human in executing various tasks. The work will involve multi-domain research including planning and control, manipulation, and possibly vision/perception. The methods will be implemented and evaluated in high performance simulators and (time-permitting) in actual robotic platforms. The results of the interns are expected to be published in top-tier robotic conferences and/or journal.

The internship should start in January 2025 (exact date is flexible) with an expected duration 3-6 months depending on agreed scope and intermediate progress.

Required Specific Experience

• Current/Past enrollment in a PhD program in Mechanical, Aerospace, Electrical Engineering, with a concentration in Robotics
• 2+ years of research in at least some of: machine learning, optimization, control, path planning, computer vision
• Experience in design and simulation tools for robotics such as ROS, Mujoco, Gazebo, Isaac Lab
• Strong programming skills in Python and/or C/C++

Additional Desired Experience

• Development of planning and control methods in robotic hardware platforms
• Acquisition and processing of multimodal sensor data, including force/torque and proprioceptive sensors
• Prior experience in human-robot interaction, legged locomotion, mobile manipulation

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 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 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 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.

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.