We have a postdoc position in an exciting multidisciplinary project to develop an innovative smart shoe system for ankle injury prevention, which is funded by the National Institute of Arthritis and Musculoskeletal and Skin Disease (NIAMS). The smart shoe system integrates machine learning-based wearable sensor data processing, a soft robotic ankle device with stiffness adjustment capability, sophisticated biomechanical models of foot-ankle mechanics, advanced injury risk prediction and control algorithms, and an intuitive user interface with real-time and summative injury risk metrics. The postdoc will work with Drs. Hyunglae Lee, Pavan Turaga, Sze Zheng Yong, and Matthew Buman and multiple graduate students with diverse backgrounds in biomechanics, soft robotics, controls, machine learning, and behavioral science. The position is available to start immediately.


  • Development of machine learning algorithms (especially, deep learning) for robust sensor data processing and activity recognition
  • Design and implementation of prediction and control algorithms to provide active assist-as-needed ankle support
  • Integration of the machine learning and control algorithms into the soft robotic ankle device
  • Device testing, validation, and troubleshooting
  • Subject recruitment, design of experiment, biomechanics data collection, processing, analysis, and interpretation
  • Participation in user studies for the design of a mobile app to support the smart shoe system
  • Mentoring graduate students for seamless integration of device development and machine learning and control algorithms
  • Presentation and publication of results in robotics, controls, and/or ML conferences and journals

Eligibility and Qualifications

  • Must have a PhD in Mechanical Engineering, Robotics, Computer Science, or related field
  • Must have research achievements and interest in one or more of the above-mentioned topics (i.e., soft robotics, controls, and machine learning)
  • Advanced programming skills (e.g., MATLAB/Simulink, Python, C/C++, ROS)
  • Strong hands-on debugging and troubleshooting skills with sensors and robotic devices
  • Candidates who have experience with motion analysis equipment (e.g., motion capture system, force plate, IMUs, insole sensors) and system identification for physiological systems are particularly welcomed

Application deadline

  • On a rolling basis

To be considered for this position, please email Dr. Hyunglae Lee (email: with 1) CV/Resume, 2) transcripts, 3) a single-page statement of purpose, and 4) a list of two or three references