We are seeking a graduate student interested in helping advance the fields of source separation, speech enhancement, robust ASR, and sound event detection/localization in challenging multi-source and far-field scenarios. The intern will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for publication. The ideal candidate would be a senior Ph.D. student with experience in some of the following: audio signal processing, microphone array processing, probabilistic modeling, sequence to sequence models, and deep learning techniques, in particular those involving minimal supervision (e.g., unsupervised, weakly-supervised, self-supervised, or few shot learning). The internship will take place during spring/summer 2022 with an expected duration of 3-6 months and a flexible start date. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

We are looking for a graduate student interested in helping advance the field of multi-modal scene understanding, with a focus on detailed captioning of a scene using natural language. The intern will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for publication. The ideal candidate would be a senior Ph.D. student with experience in deep learning for audio-visual, signal, and natural language processing. The expected duration of the internship is 3-6 months, and 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.

MERL is looking for a self-motivated intern to work on problems at the intersection of video understanding, audio processing, and language models. The ideal candidate would be a senior PhD student with a strong background in machine learning and computer vision (as demonstrated via top-tier publications). The candidate must have prior experience in developing deep learning methods for audio-visual-language data. Expertise in popular embodied AI environments as well as a strong background in reinforcement learning will be beneficial. The intern is expected to collaborate with researchers in computer vision and speech teams at MERL to develop algorithms and prepare manuscripts for scientific publications. This internship requires work that can only be done at MERL.