AI/ML Research Assistant

Pay rate : $27 an hour Position Summary The Human Performance Laboratory and Center for Lower Extremity Ambulatory Research (CLEAR) at Rosalind Franklin University of Medicine and Science is seeking a highly motivated Artificial Intelligence (AI)/Machine Learning (ML) Research Assistant. The ideal candidate will have a strong background in the application of AI and ML approaches to human movement sciences, biomechanics, and/or kinesiology/exercise/sport science, particularly as it relates to gait and posture in healthy and patient populations. The Research Assistant will work under the direct supervision of Dr. Noah Rosenblatt. The candidate must be proficient in C++, R, Python, MATLAB, or another similar coding program and have experience working with, or a strong desire to work with, data sets captured from patients with diabetes and/or users of orthotics and prosthetics. While the primary responsibilities of the Research Assistant will relate to data analysis, they will have the opportunity to participate in all aspects of our research program, including project conception, experimental design and execution, and will receive broad training and mentorship in human subjects research. This position is anticipated to last 12 - 16 weeks depending on performance. While this position is not eligible for our regular employee benefits, all RFU employees are eligible to accrue personal leave in accordance with IL law. All RFU employees are also eligible to make unmatched contributions to voluntary supplemental pre-tax and post-tax retirement plans. Who We Are Rosalind Franklin University of Medicine and Science (RFU) is a six-college graduate health sciences university dedicated to the interprofessional education of health and biomedical professionals. Dr. William M. Scholl College of Podiatric Medicine – Scholl is the name to know in podiatric medicine. Founded in 1912 by a pioneer in podiatry, Scholl College is an international resource in the field. In fact, one out of three podiatrists in the U.S. is a Scholl College alumnus. As part of Rosalind Franklin University (RFU), we provide a fully accredited, four-year professional program leading to the Doctor of Podiatric Medicine (DPM) degree, along with opportunities for a combined DPM/PhD degree. Scholl prepares tomorrow’s top physicians and leaders in the field of podiatry. Scholl College is recognized for its exceptional clinical education, in part because of the RFU Health Clinics adjacent to the RFU campus. We have access to clinical experiences in a variety of healthcare settings throughout the Chicago metro area, where our students participate in multiple rotations at the John H. Stroger, Jr. Hospital of Cook County and other leading Chicago metro area teaching hospitals. Furthermore, our campus offers a 100,000-square-foot Innovation and Research Park to encourage advancements in podiatric medicine and other healthcare specializations – and foster cross-disciplinary collaboration. Founded in 2004, CLEAR’s mission is to make measurable improvements in the health and function of the lower extremities by using multidisciplinary approaches to biomedical engineering, clinical & basic sciences, and health services. CLEAR serves as one of the nation’s most productive and well-known interdisciplinary podiatric research centers as evidenced by its exemplary publication record in diverse journals and a history of industry and federally funded projects from agencies including the National Institutes of Health, the Department of Defense, the National Aeronautics and Space Administration, and the American Diabetes Association. Ongoing research efforts within CLEAR are focused on: biomechanics of locomotion; fall-risk assessment; fall-prevention; human performance optimization; diabetic wound healing; promoting mobility across diverse populations including older adults, patients with diabetes, patients with traumatic brain injury, patients with obesity, and patients with lower extremity amputation; injury prevention; material testing of prosthetic and orthotic devices. Essential Duties & Responsibilities The primary responsibility of the Research Assistant relates to the conduct of an ongoing study attempting to identify gait differences in individuals with a history of victimization. Efforts specific to this project will include: Developing AI models to identify group differences in complex movement patterns Summarizing process and results in manuscript form for submission of publication Creating visualizations of movement data for use in videos that will be integrated into online surveys in which viewers rate the videos Integrating visualizations into survey software and ensuring compatibility and consistency of visualization viewing across all platforms Employ automatic eye tracking options into survey and assist in developing analysis codes for the data Contribute to the activities of ongoing research projects of all CLEAR membersAssist in data collection and post-processing of data for other ongoing projects Evaluate existing AI-based solutions to automating detection of foot posture indices and identify opportunities for future advancements Assist in transitioning the existing scholarship reporting system to an online system capable of automatically tracking productivity based on uploaded CVs Assist with IRB documentation as it relates to ongoing projects Supervise and mentor the lab activities of graduate students, community undergraduates, and other research assistants as required. Conditions of Employment Must achieve satisfactory results from a background check Required Education & Experience Bachelor’s degree in Data Science and Analytics, Computer Engineering, Mathematics, Biomedical Engineering, or a related field. Advanced classes in current AI and ML approaches. Required Knowledge, Skills, & Abilities Developing AI pipelines for analysis of human movement data Programming experience Ability to multitask Ability to work independently Preferred Qualifications Experience in, or strong understanding of, collection of human movement data using a motion capture system. Experience in designing and troubleshooting systems for analysis of human movement data Knowledge of inertial sensors, including the processing and analysis of raw data Knowledge and ability to independently implement statistical analyses. Excellent analytical and problem-solving skills Ability to excel in both team and individual environments. Strong creative problem-solving Typical Physical Demands & Working Conditions Selected candidate must have the mental and physical capabilities to perform the essential functions of the position with or without reasonable accommodations. EOE, Including Disability / Vets