Research Scientist Intern, Wearable Sensing & Human Motion Analysis

This internship focuses on developing a learned model for motion analysis for swing sports as an alternative to heuristic, threshold-based methods. Research goals include: improving analysis accuracy, generalization across players and equipment, and generating interpretable feedback outputs. The intern will contribute to egocentric data collection using the team's simulator research setup, in addition to model development.

Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year. Some projects may require a minimum of 24 consecutive weeks.

Responsibilities

Operate egocentric data collections on our state-of-the-art in-lab simulator - instrument participants with Aria Gen 2 and Neural Band wearables, run capture sessions, and use the simulator's ground-truth as supervision and validation signal for your models. Design and train models over multimodal egocentric time-series: Aria Gen 2 (SLAM/IMU, eye gaze, RGB) fused with Neural Band surface EMG and wrist IMU. Tackle the core modeling problems: temporal swing phase segmentation (address → takeaway → impact → follow-through), kinematic/intent estimation from a head-mounted and wrist-worn viewpoint, and swing-quality scoring. Build an evaluation harness that benchmarks the model against the existing threshold baseline on held-out players - accuracy, phase-boundary error, and cross-subject generalization. Define the capture and labeling protocol, and handle the realities of small, noisy, personalized datasets (self-supervised pretraining, augmentation, subject-adaptive fine-tuning). Integrate the winning model back into our prototype and demo it end to end. Write up results; strong work is a candidate for publication or an internal tech report.

Qualifications

Currently has, or is in the process of obtaining a PhD degree in the domain of Computer Science, Electrical Engineering, Robotics, Biomechanics, or a related field Strong Python and hands-on experience training models in PyTorch Working knowledge of sequence/time-series modeling (temporal CNNs, transformers, state-space models, HMM/CTC-style segmentation) Experience working with real sensor data, not just curated benchmarks Comfortable running hands-on hardware experiments with human participants in a lab setting Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment Experience designing and executing your own data collections with wearable or motion-capture hardware Research experience with IMU-based human activity recognition, pose/motion estimation, or wearable biosignals Experience with surface EMG: filtering, rectification, envelope extraction, electrode-shift and cross-session variability Familiarity with egocentric perception (Project Aria, Ego4D/Ego-Exo4D, Aria Digital Twin) or multimodal sensor fusion Background in sports biomechanics or motion analysis self-supervised or few-shot learning for personalization First-author publications at CVPR/ICCV/ECCV/NeurIPS/ICLR, or in biomechanics and wearables venues Plays swing sports - genuinely helpful for labeling and sanity-checking feedback Intent to return to a degree-program after the completion of the internship