Internship - Multi-modal sensor fusion for predictive maintenance

Internship at MERL focusing on developing advanced machine learning algorithms to fuse multi-modal time sequence data for electric machine condition monitoring and predictive maintenance. The ideal candidate is a self-motivated Ph.D. candidate in Computer Science, Electrical Engineering, or a related field, with a strong background in machine learning and signal processing and a proven publication record. Experience in time-series analysis, multimodal sensor fusion, or physics-informed machine learning is preferred, and knowledge of electric machines is a plus. The intern will collaborate with MERL researchers to design and develop novel algorithms, prepare technical reports, and contribute to manuscripts for top-tier scientific publications. This position requires onsite work at MERL, with a flexible start date. In addition to base pay, interns receive a relocation stipend, covered travel to and from MERL, and a monthly Charlie Card for local commuting. MERL provides immigration support for qualified candidates as needed. Employment is at-will. Location: Cambridge, MA, USA; onsite work with potential health insurance eligibility after 90 days; full authorization to work in the U.S. is required.