Internship: Residual Model Learning for Building Energy Systems at Mitsubishi Electric Research Labs

Mitsubishi Electric Research Labs, Inc. 'MERL' provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, MERL complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. 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. Responsibilities: - Work on learning residual dynamics to augment ODE/DAE-based models of building energy systems - Conduct research in system identification, optimization, machine learning and/or function approximation - Hands-on programming with numerical optimization solvers and Python - Publication in a high-tier venue as an expected outcome of the internship

Internship: Residual Model Learning for Building Energy Systems

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