MERL is seeking a highly motivated and qualified individual to conduct research in dynamic model-based robotic system design and control. The ideal candidate should demonstrate solid research record in robotic dynamics and differentiable simulation, motion planning and control, and optimization. Strong coding skill on implementing robotic dynamics and differentiable simulation/optimization using CasADi/PyTorch is 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.

MERL is seeking a motivated and qualified individual to conduct research on transfer learning for fault diagnosis, to be used for industrial applications especially electric machine fault diagnosis and predictive maintenance. Ideal candidates are Ph.D. students with a solid background and publication record in one or more research areas: fault diagnosis, statistical machine learning, transfer learning and domain adaptation, and electric motors. Strong programming skills using Python/PyTorch are expected. Knowledge and background in electric machines related research is a strong plus. Start date for this internship is flexible and the duration is typically 3 months.

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 looking for a self-motivated intern to work on time sequence data analysis for denoising, feature extraction, and machine learning. The ideal candidate would be a Ph.D. candidate in computer science or electrical engineering with solid research background in signal processing and machine learning. Experience with time sequence data processing is preferred. Proficiency in MATLAB 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 looking for a self-motivated intern to work on sensing data fusion with applicatino to condition 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 signal processing and machine learning. Background in electric machine, system control and automation is preferred. Proficiency in MATLAB 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 3-6 months and the start date is flexible. This internship requires work that can only be done at MERL.

MERL is seeking an intern to work on blockchain based solutions for factory automation. The ideal candidate will have experience implementing a blockchain network with Hyperledger Fabric, have experience working with smart contracts, and be fluent in C++ and Go. Experience developing applications in an embedded linux environment is highly desirable. We are looking for someone to start as soon as possible and the duration is 3-4 months.

MERL is seeking a highly motivated and qualified individual to conduct research on DER-centric Microgrid Optimization and Control. Ideal candidate should be a senior Ph.D. student with solid background and publication record in any of the following, or related areas: power systems, Microgrid, distribution energy resources, host capacity, optimization, and uncertainty modelling. Hand-on experience on programming using Python, C/C++, or MATLAB is required. The duration of the internship is anticipated to be 3-6 months, and the start date is flexible.