The Human Health & Performance Systems Group develops human-centered technologies to overcome operational challenges and to enhance human capability in domains of interest to national security. Our research programs focus on innovative and objective solutions in the areas of integrated wearable systems, human-machine teaming, enhanced communications, neurocognitive analytics, and medical technologies. Our group is highly interdisciplinary and includes scientific experts in physiology, cognitive science, neuroscience, psychology, biomechanics, computer science, engineering, and physics. Our core technical competencies include system-level modeling and gap analysis, advanced sensing and signal processing, machine learning and artificial intelligence, computational modeling, hardware and software prototyping, model-based systems engineering, and human data collection in laboratory and field environments.

Position Description

Our team is looking for a Co-Op student with an interest in solving challenging AI/ML problems using biomedical signal processing and wearable technology.

Through this opportunity, you will work with a multi-disciplinary team

consisting of engineers, scientists, and clinicians to prepare and process large biomedical and physiological datasets (e.g.,PPG, accelerometry, EOG, EEG, commercial-off-the-shelf wearable data, etc), develop and evaluate machine learning algorithms, and implement data visualization tools for advanced prediction and inference of physiological status (i.e. fatigue, illness, stress, etc). We are looking for students who are self-motivated

and interested in signal processing, machine learning, deep learning, statistical pattern recognition, and high-performance computing.

Requirements/Skills
- The candidate is a student in a B.S., M.S., or Ph.D. program in Biomedical Engineering, Electrical Engineering, Computer Science, or other relevant degree.
- Experience with Python, MATLAB and machine learning (coursework or practical)

Group 2-24 | Co-Op | Biomedical

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