We are seeking a graduate student interested in helping advance the fields of source separation, speech enhancement, 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 audio signal processing, microphone array processing, probabilistic modeling, and deep learning techniques requiring minimal supervision (e.g., unsupervised, weakly-supervised, self-supervised, or few shot learning). The internship will take place during fall/winter 2021 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.

MERL is looking for interns to work on fundamental research in the area of end-to-end speech and audio processing for new and challenging environments using advanced machine learning techniques. The intern will collaborate with MERL researchers to derive and implement new models and learning methods, conduct experiments, and prepare results for high-impact publication. The ideal candidates would be senior Ph.D. students with experience in one or more of automatic speech recognition, speech enhancement, sound event detection, and natural language processing, including good theoretical and practical knowledge of relevant machine learning algorithms with related programming skills. The internship will take place during fall/winter 2021 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.