MERL seeks a highly motivated intern to develop an interface between real-time control systems that are implemented in the Modelica language, and laboratory HVAC equipment that is controlled by Labview. The control algorithms are developed using our Modelica library of HVAC components, and are realized natively in the Modelica language using the Synchronous Library. They are run in real-time on a PC using the Modelica Device Drivers library, and communicate with the Labview system via UDP. The intern would be responsible for developing professional-grade code to mature this interface, and then conduct experiments to test new control algorithms in our laboratory. Expertise using software development tools, such as Microsoft Visual Studio and network protocols such as UDP, is necessary. Experience with Modelica is strongly preferred. Knowledge and experience of vapor compression systems is also strongly preferred. Knowledge of control theory, including classical feedback and finite state machines, along with related laboratory experience is required. On-site employment is preferred, although it may be possible to conduct this work remotely. Students enrolled in a Masters or Ph.D. degree program of study are encouraged to apply. The internship is expected to be 3-6 months in duration, preferably in the fall or winter, 2020.

MERL is seeking a highly motivated intern to research multi-agent motion planning by combining optimization-based methods with machine learning. The ideal candidate is enrolled in a PhD program in Electrical, Mechanical, Aerospace Engineering, Robotics, Computer Science or related program, with prior experience in multi-agent motion planning, machine learning (especially supervised, reinforcement, and safe ML), and convex and non-convex optimization. A successful internship will result in innovative methods for multiagent planning, in the development of well-documented (Python/MATLAB) code for validating the proposed methods, and in the submission of relevant results for publication in peer-reviewed conference proceedings and journals. The expected duration of the internship is 3 months with a flexible start date in the Spring/Summer 2021. 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 looking for a highly motivated intern to conduct research on data-driven energy management strategies for (hybrid) electric vehicles. The candidate will develop methods that use data, e.g., of human drivers or traffic conditions, in order to improve the control of electric vehicles. The ideal candidate will have experience in either one or multiple of the following topics: model predictive control, machine learning, statistical learning, numerical optimization, and (inverse) optimal control. Prior experience with (hybrid) electric vehicles is a plus. Good programming skills in MATLAB, Python, or C/C++ are required. PhD students in engineering or mathematics with a focus on control theory or numerical optimization are encouraged to apply. Publication of relevant results in conference proceedings or journals is expected. The expected duration of the internship is 3-6 months. The start date 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.