MERL is seeking a motivated graduate student to research numerical methods pertaining to the simulation, control, and optimization of large-scale systems. 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 numerical methods, control, 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 learning residual dynamics to augment ODE/DAE-based models of building energy systems. The ideal candidate will have a strong understanding of system identification, optimization, machine learning and/or function approximation; additional understanding of energy systems is a plus. Hands-on programming experience with numerical optimization solvers and Python is preferred; experience with Modelica/FMUs is a plus. PhD students are strongly preferred, as an expected outcome of the internship is a publication in a high-tier venue. The minimum duration of the internship is 12 weeks; start time is flexible.

MERL is looking for a highly motivated and qualified candidate to work on modeling for smart sustainable buildings. The ideal candidate will have a strong understanding of modeling renewable energy sources, grid-interactive buildings, occupant behavior, and dynamical systems with expertise demonstrated via, e.g., peer-reviewed publications. Hands-on programming experience with Modelica is preferred. The minimum duration of the internship is 12 weeks; start time is flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

MERL is seeking highly motivated and qualified interns to work on efficient machine learning techniques. The ideal candidates would have significant research experience in federated learning, generative large language models, and efficient/green AI. A mature understanding of modern machine learning methods, proficiency with Python, and familiarity with deep learning frameworks are expected. Candidates at or beyond the middle of their Ph.D. program are encouraged to apply. The expected duration is 3 months long with flexible start dates.

MERL is seeking an intern to work on research for quantum machine learning (QML). The ideal candidate is an experienced PhD student or post-graduate researcher having an excellent background in quantum computing, deep learning, and signal processing. Proficient programming skills with PyTorch and PennyLane will be additional assets to this position.

MERL is looking for a self-motivated intern to work on electric machine monitoring, fault detection, and predictive maintenance. The ideal candidate would be a Ph.D. candidate in electrical engineering or computer science with solid research background in electric machines, signal processing, and machine learning. Proficiency in MATLAB and Simulink is necessary. The intern is expected to collaborate with MERL researchers to perform simulations, analyze experimental data, and prepare manuscripts for scientific publications. The total duration is anticipated to be 3 months and the start date is flexible. This internship requires work that can only be done at MERL.

MERL is seeing a motivated and qualified individual to conduct research on electric motor design and modeling, with a strong focus on electromagnetic analysis. Ideal candidates should be Ph.D. students with solid background and publication record in one more research area on electric machines: electric and magnetic modeling, new machine design and prototyping, harmonic analysis, fault detection, and predictive maintenance. Research experiences on modeling and analysis of electric machines and fault diagnosis are required. Hands-on experience with new motor design and data analysis techniques are highly desirable. Start date for this internship is flexible and the duration is 3-6 months.

MERL is seeking a highly motivated and qualified individual to conduct research/development in speed-sensorless control of electrical machines.



The ideal candidate should have solid backgrounds in electrical machines, sensorless drives control, dynamical system analysis, signal processing, state estimation, and parameter identification. Demonstrated knowledge of the state-of-the-art sensorless drives control and experience on using dSPACE for real-time HIL experimentation is necessary. Proven record of publishing results in leading conferences/journals is a plus.



Senior Ph.D. students in electrical engineering, control, and related areas are encouraged to apply. Start date for this internship is as soon as possible and the duration is about 3-6 months.

The Computational Sensing team at MERL is seeking motivated and qualified individuals to assist in the development of computational methods for a variety of sensing applications. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: imaging inverse problems, deep learning for inverse problems, large-scale optimization, blind inverse scattering, radar/lidar/THz imaging, joint communications and sensing, multimodal sensor fusion, object or human tracking, sensing of dynamical systems, or wave-based inversion. Experience with experimentally measured data is desirable. Publication of the results produced during our internships is expected. The duration of the internships is anticipated to be 3-6 months. Start date is flexible.

The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that can perform background oriented Schlieren (BOS) tomography. The project goal is to utilize both analytical and learning-based architectures to enable the reconstruction of 3D air flows in an indoor setting from BOS measurements. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: imaging inverse problems, large-scale optimization, learning-based modeling for imaging, Schlieren tomography, physics informed neural networks. Publication of the results produced during our internships is expected. The duration of the internships is anticipated to be 3-6 months. Start date is flexible.

The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in THz (Terahertz) sensing. Expertise in statistical inference, unsupervised anomaly detection, and deep learning (spatial-temporal representation learning) is required. Previous hands-on experience in THz data analysis is a plus. Familiarity with python and deep learning libraries is a must. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments with collaborators, and prepare results for patents and publication. The expected duration of the internship is 3 months with a flexible start date.

MERL is seeking a motivated intern to assist in developing hardware and algorithms for multimodal imaging applications. The project involves integration of radar, camera, and depth sensors in a variety of sensing scenarios. The ideal candidate should have experience with FMCW radar and/or depth sensing, and be fluent in Python and scripting methods. Familiarity with optical tracking of humans and experience with hardware prototyping is desired. Good knowledge of computational imaging and/or radar imaging methods is a plus.

MERL is looking for a highly motivated and qualified intern to work on 6D pose estimation and tracking of multiple objects for robotic manipulation. The ideal candidate would be a Ph.D. student familiar with the state-of-the-art methods for 6D pose estimation, tracking of rigid objects and vision based scene understanding. The candidate should have a strong background in deep learning using unsupervised and self-supervised approaches and geometric scene understanding. The internship requires developing and implementing algorithms to estimate accurate 6D poses of multiple objects in a scene captured by an RGB-D camera with unknown positions. The method will be applied for robotic manipulation where the knowledge of accurate position and orientation of objects within the scene would allow the robot to interact with the objects. Experience working with a physics engine simulator like PyBullet, Issac Gym, or Mujoco is preferred. Proficiency in Python programming is necessary, and experience with ROS is a plus. The successful candidate will collaborate with MERL researcher, and publication of the relevant results is expected. Start date in the summer or early fall is preferable, and the expected duration of the internship is 3-4 months. Interested candidates are encouraged to apply with their recent CV and list of publications in related topics.

MERL is looking for a highly motivated individual to work on robust control of robotic manipulation system using vision and tactile feedback. The research will develop complex system using optimization, contact sensing and control techniques that can be used for closed-loop control of manipulation systems. The ideal candidate should have experience in either one or multiple of the following topics: optimization for contact-rich systems, manipulation, grasping and tactile sensing. 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. A successful internship will result in submission of results to peer-reviewed conference and journals. Good coding skills in Python and state-of-the-art optimization packages like IPOPT, SNOPT, etc. is required. The expected duration of internship is 3-4 months with start date in Aug/Sept 2023. This internship is preferred to be onsite at MERL.

MERL is seeking a highly motivated candidate to collaborate with the Control for Autonomy team in research and experimentation on control and planning for legged robots. The ideal candidate is expected to be working towards an MS or PhD with emphasis on control or related area, and it is a merit to have interest and background in one or several of: experimentation and research on locomotion of legged robots, model predictive control, statistical estimation, machine learning, numerical optimization, motion planning, SLAM. Good programming skills in MATLAB, ROS, Python, are required and knowledge of C++ is a merit. The expected duration of the internship is 3 months with a start date of late fall 2022/early winter 2023.

MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on planning and control for autonomous vehicles in uncertain surrounding environments. The research domain includes algorithms for path planning and control in environments that are uncertain and perceived by sensing and predicted according to models and data. The ideal candidate is expected to be working towards a PhD with strong emphasis in vehicle guidance and control, and to have interest and background in as many as possible of: vehicle dynamics modeling and control, sensor uncertainty modeling, data-driven prediction, predictive control for uncertain systems, motion planning. Good programming skills in MATLAB, Python are required, knowledge of C/C++, rapid prototyping systems, automatic code generation, vehicle simulation packages (CarSim, CarMaker) or ROS are a plus. The expected start of of the internship is in the late Spring/Early Summer 2022, for a duration of 3-6 months.