A position for Postdoctoral Research Fellow is available at the University of Michigan, Mechanical Engineering Department. Priority will be given to applicants interested in pursuing the Schmidt AI in Science Postdoctoral Fellowship, which itself has a priority date of Nov 30, 2023.
The candidate will develop AI frameworks and models for assessing a personalized balancing training technology. Age-related declines in balance function drastically impact quality of life and present long-term care challenges; falls are the leading cause of fatal and non-fatal injuries among older adults (OAs) greater than 65 yrs. Successful fall prevention programs include balance exercise regimes, designed to recover, retrain, or develop new sensorimotor strategies to facilitate functional mobility. To enable preventative and therapeutic at-home balance training, we aim to develop AI capabilities for automatically evaluating balance and recommending the progression of next exercises. We will approach this using deep reinforcement learning. These AI developments are conducted with access to sensor data obtained from real-life experiments of different OAs performing the balance exercises.
To apply:
Interested applicants please send your CV, representative publications, and contact information for three references to Prof. Xun Huan (xhuan@umich.edu). Review of applications will begin immediately and will continue until the position is filled. Priority will be given to applicants interested in pursuing the Schmidt AI in Science Postdoctoral Fellowship, which itself has a priority date of Nov 30, 2023.
Required Qualifications:
- PhD in a field of data science, applied mathematics, statistics, computer science, or related engineering or science subject area
- Technical expertise in ML/AI
- Experience or strong interest in physical therapy, human movement science, or balance-related research
- Publication record indicative of relevant research expertise
- Excellent interpersonal, written and oral communication skills
Desired Qualifications:
- The use of state-of-the-art ML/AI tools
- Knowledge and expertise in Python, MATLAB, R, Julia, C, C++, or related languages
- Experience in high-performance, distributed, or parallel computing
- Knowledge and expertise in computational science and software development
- Ability to work in collaborative, interdisciplinary research environments on problems comprising diverse application domains
- A background in solving practical problems in science and engineering that involve encounters with real-world data