The University of Waterloo is inviting applications for a fully funded PhD position focused on predicting and overcoming freezing of gait (FOG) in Parkinson's disease using exoskeletons and sensor fusion. This interdisciplinary research opportunity sits at the intersection of neuroscience, biomechanics, wearable robotics, and data science. The successful candidate will be co-supervised by Professors Kaylena Ehgoetz Martens and Arash Arami, and will work in a collaborative environment that values equity, diversity, and inclusion.

The project aims to advance the prediction and understanding of FOG by integrating exoskeleton technology with multimodal sensor fusion. Key research components include experimental study of gait, integration of wearable sensors with exoskeleton systems, sensor fusion and machine learning for FOG prediction, and validation in both laboratory and real-world settings. The research will involve close collaboration with engineers, clinicians, and people living with Parkinson’s disease, ensuring strong translational and patient-centered impact.

Applicants should have backgrounds in movement science, kinesiology, rehabilitation science, neuroscience, biomedical engineering, robotics, mechatronics, wearable technology, human-machine interaction, signal processing, sensor fusion, machine learning, data science, or clinical research related to Parkinson’s disease or gait disorders. Desired skills include experience with wearable sensors and gait analysis, human-subject experimentation, programming (Python, MATLAB, R, C/C++), and interdisciplinary research.

The position offers access to state-of-the-art exoskeletons, wearable sensors, and motion analysis tools, as well as opportunities for international collaboration, conference presentations, and high-impact publications. The PhD is fully funded through research and institutional support, with additional opportunities for scholarships such as CIRTA, NSERC, and OGS. To apply, email [email protected] with the subject 'FOG-Exo PhD position' and include a motivation letter, CV, transcripts, and relevant publications.