Internship Openings

MERL is seeking a highly motivated and qualified intern to work on anomaly detection for operational technology security. The ideal candidate would have significant research experience in anomaly detection, machine learning, and cybersecurity for operational technology. A mature understanding of modern machine learning methods, proficiency with Python and PyTorch, and a relevant research publication history are expected. Candidates at or beyond the middle of their Ph.D. program are encouraged to apply. The expected duration is for 3 months with flexible start dates (but ideally in December or early January).

Required Specific Experience

• Proficiency with PyTorch framework.
• Research publications in machine learning and anomaly detection.

MERL is seeking a highly motivated and qualified intern to conduct research on IoT network anomaly detection and analysis. The candidate is expected to develop innovative anomaly detection technologies that can proactively detect and analyze network failure in large-scale IOT networks. The candidate should have knowledge of LLM/ML and anomaly detection. Knowledge of network log analysis and network protocol a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. Start date for this internship is flexible and the duration is 3 months.

The responsibilities of this intern position include (i) research on anomaly detection in large-scale IoT networks; (ii) develop proactive network anomaly detection and analysis technologies; (iii) simulate and analyze the performance of developed technology.

We are seeking graduate students interested in helping advance the fields of sound event detection/localization, anomaly detection, and physics informed deep learning for machine sounds. The interns will collaborate with MERL researchers to derive and implement novel algorithms, record data, conduct experiments, integrate audio signals with other sensors (electrical, vision, vibration, etc.), and prepare results for publication. Internships regularly lead to one or more publications in top-tier venues, which can later become part of the intern's doctoral work.

The ideal candidates are senior Ph.D. students with experience in some of the following: audio signal processing, microphone array processing, physics informed machine learning, outlier detection, and unsupervised learning.

Multiple positions are available with flexible start dates (not just Spring/Summer but throughout 2025) and duration (typically 3-6 months).

Mitsubishi Electric Research Laboratories (MERL) in Cambridge, MA, is seeking a highly motivated and qualified individual to join our summer internship program and conduct cutting-edge research on hybrid AC/DC power systems. The ideal candidate should be a senior or junior Ph.D. student in Electrical Engineering or a related field, with in-depth knowledge of AC and DC power systems, renewable generation, power electronics, and power system analysis and control. Strong programming skills in MATLAB, Python, or C/C++ are required. The expected duration of the internship is 3-4 months, and the start date is flexible.

MERL is looking for a self-motivated intern to work on multi-modal sensor fusion for health condition monitoring and predictive maintenance of motor drive systems. The ideal candidate would be a Ph.D. candidate in electrical engineering or computer science with solid research background in signal processing and machine learning. Experience in motor drive system is a plus. The intern is expected to collaborate with MERL researchers to collect data, explore multi-modal data relationship, and prepare manuscripts for publications. The total duration is anticipated to be 3 months and the start date is flexible.

Required Specific Experience

• Experience with multi-modal sensor fusion.

MERL seeks graduate students passionate about deep learning and energy systems to contribute to the development of deep time-series forecasting models for real building energy data. The work will involve multi-domain research including deep learning model development, time-series analysis, and possibly integration with energy management systems. The methods will be implemented and evaluated using real-world datasets. The results of the internship are expected to be published in top-tier machine learning and energy systems conferences and/or journals.

Exact start date is flexible (most likely Summer 2025), with an expected duration of 3-6 months, depending on agreed scope and intermediate progress.

Required Specific Experience:

• Current or past enrollment in a PhD program in Electrical Engineering, Computer Science, or a related field with a focus on Machine Learning or Energy Systems.
• 2+ years of research experience in at least some of the following areas: deep learning, time-series analysis, probabilistic machine learning, energy systems modeling.
• PyTorch fluency.
• Familiarity with real-world data wrangling.
• Experience with time-series data visualization and analysis tools.

Strong Pluses:

• Familiarity with transformer-based time-series forecasting methodologies e.g. TFT or time-series foundation models.
• Familiarity with adaptation mechanisms e.g. fine-tuning, meta-learning.