Research Intern - Audio Hardware

The Meta Reality Labs Research Team brings together a world-class team of researchers, developers, and engineers to create the future of virtual and augmented reality, which together will become as universal and essential as smartphones and personal computers are today. Just as personal computers have done over the past 45 years, AR, VR and MR will ultimately change everything about how we work, play, and connect. We are developing all the technologies needed to enable breakthrough AR glasses and VR headsets, including optics and displays, computer vision, audio, graphics, brain-computer interfaces, haptic interaction, eye/hand/face/body tracking, perception science, and true telepresence. Some of those will advance much faster than others, but they all need to happen to enable AR, VR and MR that are so compelling that they become an integral part of our lives. In particular, the Meta Reality Labs Research Audio team is focused on two goals; creating virtual sounds that are perceptually indistinguishable from reality and redefining the human hearing. These two initiatives will allow us to connect people by allowing them to feel together despite being physically apart and allow them to converse in even the most difficult listening environments. The Devices and Hardware Research team of the Reality Labs Research Audio is looking for innovative PhD student interns interested in one or more of the following areas: > Electrophysiological and bio-signal sensing technologies and the associated signal processing — EEG, EMG, EOG, ECG, PPG, EDA, and other wearable biopotential/physiological modalities, including electrode and front-end design, artifact rejection, and robust on-body signal quality. > Extremely low-power, energy-autonomous wearable design — sub-milliwatt and duty-cycled sensing architectures, analog/mixed-signal front ends, event-driven and always-on wake-up circuits, and self-powered operation via energy harvesting. > Embedded real-time signal processing on resource-constrained hardware — ANC, echo cancellation, hear-through, adaptive filtering, and efficient implementations on MCUs and DSPs. > Hearing biomechanics characterized through experimentation and numerical simulation. > Conventional and ML-based signal processing — feature extraction, classification, regression and event detection, sequence/temporal modeling, self-supervised and transfer learning for scarce physiological labels, subject-independent generalization and calibration, sensor fusion across modalities, and on-device/TinyML inference (quantization, pruning, distillation) under tight compute and energy budgets. This internship can be twelve (12) to twenty-four (24) weeks, with various start dates throughout the year.