Artificial Intelligence

MERL is seeking a motivated and qualified intern to conduct research on machine learning based electric motor design and optimization. Ideal candidates should be Ph.D. students with a solid background and publication record in electric machine design, optimization, and machine learning. Hands-on experience with the implementation of optimization algorithms, machine learning and deep learning methods is required. Strong programming skills using Python/PyTorch are expected. Knowledge and experience with electric machine principle, design and finite-element analysis are highly desirable. Start date for this internship is flexible and the duration is about 3 months.

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).

We seek a highly motivated intern to conduct original research in generative models for instructional video generation. We are interested in applications to various tasks such as video generation from text, images, and diagrams. The successful candidate will collaborate with MERL researchers to design and implement novel models, conduct experiments, and prepare results for publication. The candidate should be a PhD student (or recent graduate) in computer vision and machine learning with a strong publication record including at least one paper in a top-tier computer vision or machine learning venue such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, AAAI, or TPAMI. Strong programming skills, experience developing and implementing new models in deep learning platforms such as PyTorch, and broad knowledge of machine learning and deep learning methods are expected, including experience in the latest advances in video generation. Start date is flexible; duration should be at least 3 months.

Required Specific Experience

• Experience with video diffusion models, LLMs, and Vision-and-Language Models.
• Experience developing and implementing new models in PyTorch
• At least one paper in a top-tier computer vision or machine learning venue such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, AAAI, or TPAMI.
• Ph.D. student in computer vision or a related field.