MERL is looking for a highly motivated and qualified candidate to work on Bayesian Optimization and predictive control for net-zero energy buildings. The ideal candidate will have a strong understanding of control, optimization, and/or machine learning with expertise demonstrated via, e.g., publications, in at least one of: Bayesian optimization, (stochastic) model predictive control, reinforcement learning, controller tuning; additional understanding of energy systems is a plus. Hands-on programming experience with numerical optimization solvers and Python is preferred. 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 seeking a highly motivated and qualified individual to join our summer internship program and conduct research in the area of power system operation and control. The ideal candidate should have a solid knowledge of power distribution systems, power system state estimation, signal processing, and mathematical optimization. Experience with MATLAB or C/C++/Python is required. The duration of the internship is expected to be 3-6 months, and the start date is flexible. Senior Ph.D. students or Ph.D. holders specializing in electrical engineering or related areas are encouraged to apply.

MERL is seeking an undergraduate or master student engineering intern to use MERL''s millimeter radar hardware testbed for experiments and data collection, and to maintain the data preprocessing pipeline from raw data acquisition, data organization, annotation, sanitization, and document preparation. Scripting in Python/MATLAB is required. Previous experience with radio frequency (RF) testbed/evaluation kits is preferred. The duration is from September to December with a flexible start date and a part-time option.

MERL is looking for a self-motivated intern to work on monitoring of motor drive system via vibration analysis. The ideal candidate would be a Ph.D. candidate in electrical engineering or mechanical engineering with solid research background in signal processing, vibration analysis, and mechanical system. Proficiency in Matlab or other related software is necessary. Experience in machine learning is a plus. The intern is expected to collaborate with MERL researchers to perform model simulation, data analysis, 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 seeking a highly motivated and qualified intern to work on robust AI for cybersecurity systems. The intern will be expected to conduct research on enhancing the resilience of cybersecurity systems against advanced attacks. The ideal candidate would have significant research experience in the following topics: robust machine learning and anomaly detection, defenses against adversarial examples, and cybersecurity event data processing. A mature understanding of modern machine learning methods, proficiency with Python, and familiarity with deep learning frameworks are expected. Proficiency with other programming languages and software development experience is a plus. Candidates at or beyond the middle of their Ph.D. program are encouraged to apply. This position will be available for Summer/Fall of 2023, with an expected duration of 3 months and flexible start dates.