Internship Openings

MERL is seeking a highly motivated graduate student to develop high-fidelity full-stack GNC simulators for space applications. The ideal candidate has strong experience with rendering engines, synthetic image generation, and computer vision, as well as familiarity with spacecraft dynamics, motion planning, and state estimation. The developed software should allow for closed-loop execution with the synthetic imagery, and ideally allow for real-time visualization. Publication of results produced during the internship is desired. The expected duration of the internship is 3-6 months with a flexible start date.

Required Specific Experience

• Current enrollment in a graduate program in Aerospace, Computer Science, Robotics, Mechanical, Electrical Engineering, or a related field

• Experience with one or more of Blender, Unreal, Unity, along with their APIs

• Strong programming skills in one or more of Matlab, Python, and/or C/C++

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 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 research intern to develop efficient transformer-informed stochastic MPC to control net-zero energy buildings. This is an exciting opportunity to make a real impact in the field of cutting-edge deep learning and predictive control on a real system. Publication of results produced during the internship is desired. The expected duration of the internship is 3-6 months with a flexible start date.

The Ideal Candidate Will Have:

• Significant hands-on experience with stochastic MPC
  • Publications in SMPC are a strong plus
• Fluency in Python and PyTorch
• Understanding of probabilistic time-series prediction
• Experience with convex programming
  • Convex formulations of MPC/SMPC problems are a strong plus
• Completed their MS, or >50% of their PhD program

Mitsubishi Electric Research Labs, Inc. "MERL" provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, MERL complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

MERL expressly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. Improper interference with the ability of MERL’s employees to perform their job duties may result in discipline up to and including discharge.

Working at MERL requires full authorization to work in the U.S and access to technology, software and other information that is subject to governmental access control restrictions, due to export controls. Employment is conditioned on continued full authorization to work in the U.S and the availability of government authorization for the release of these items, which might include without limitation, obtaining an export license or other documentation. MERL may delay commencement of employment, rescind an offer of employment, terminate employment, and/or modify job responsibilities, compensation, benefits, and/or access to MERL facilities and information systems, as MERL deems appropriate, to ensure practical compliance with applicable employment law and government access control restrictions.

MERL is seeking a motivated and qualified individual to conduct research on physics informed neural network-based modeling for electric motor design optimization. Ideal candidates should be Ph.D. students with solid background and proven publication record in one or more of the following research areas: 2D/3D electromagnetic modeling and simulation, analytical modeling methods for electromagnetics and iron losses (e.g. magnetic equivalent circuit), and machine learning-based surrogate modeling. Strong coding skill with ANSYS or open-source FEM software and Python-based learning library is a must and prior experience with running jobs over cluster is a plus. Start date for this internship is flexible and the duration is 3-6 months.

Required Specific Experience

• Experience with modeling and simulations for motor design

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 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.