MERL is looking for a highly motivated individual to work on tailored computational algorithms and applications of mixed-integer programming for decision making, motion planning and control of hybrid systems. The research will involve the study and development of numerical optimization techniques and/or the implementation and validation of algorithms for industrial applications, e.g., related to autonomous driving and robotics. The ideal candidate should have experience in either one or multiple of the following topics: branch-and-bound type methods, heuristics for mixed-integer programming (pre-solve, cutting planes, warm starting, integer-feasible solutions), modeling and formulation of MIPs for hybrid control systems, convex and non-convex optimization, machine learning and real-time optimization. PhD students in engineering or mathematics, especially with a focus on mixed-integer programming or numerical optimization, are encouraged to apply. Publication of relevant results in conference proceedings and journals is expected. Capability of implementing the designs and algorithms in MATLAB/Python is expected; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3-6 months and the start date is flexible.

MERL is seeing a motivated and qualified individual to conduct research on electric machine fault analysis and detection. The ideal candidate should have solid background in electric machine theory, modeling, numerical analysis, operation, and fault detection techniques, including machine learning. Research experiences on modeling and analysis of electric machines and fault detection are required. Hands-on experience with permanent magnet motor design and analysis, and knowledge on machine learning are desirable. Senior Ph.D. students in related expertise are encouraged to apply. Start date for this internship is flexible.

MERL is seeking a highly motivated and qualified intern to collaborate with the Multiphysical Systems (MS) team in research on unsupervised/semi-supervised learning using data from real building energy systems. The ideal candidate is expected to be working towards a Ph.D. in deep learning for time-series, with special interest in learning representations for deep clustering. Fluency in Python and either PyTorch/Tensorflow is required. Previous peer-reviewed publications in related research topics and/or experience with mining from real-world data is preferred. The minimum duration of the internship is 12 weeks; start time is flexible.