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Postdoctoral Fellow in Contactless Vital Sign Monitoring
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University of Arizona
Tucson, AZ (In Person)
$76,000 Salary, Full-Time
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Job Description
The Center for Telemedicine and Digital Health at the University of Arizona is seeking a highly motivated Postdoctoral Fellow with expertise in RF sensing, radar signal processing, embedded systems, or biomedical signal analysis to lead the development of next‑generation contactless monitoring technologies for heart rate, respiratory rate, and heart sounds. The fellow will design, prototype, and validate radar‑based systems capable of extracting micro‑motion signatures of cardiopulmonary activity in real‑world environments. This position is ideal for an engineer or scientist excited about translational research at the intersection of radar engineering, physiology, and AI‑driven health monitoring . Key Responsibilities Radar system development — design, configure, and optimize mmWave/FMCW/UWB radar platforms for near‑field physiological sensing. Signal processing for micro‑motion extraction — develop algorithms for phase demodulation, clutter suppression, heart‑sound extraction, and time‑frequency analysis. Machine learning for physiologic time series — build and evaluate ML/DL models for HR, RR, and heart‑sound estimation. Embedded systems & IoT integration — implement real‑time acquisition and edge processing on microcontrollers or SoCs. Experimental design & clinical validation — lead data collection studies, synchronize radar with ECG/PPG/phonocardiography, and perform statistical validation. Scientific dissemination — publish in top journals, present at conferences, and contribute to grant proposals. Collaboration — work closely with electrical engineers, biomedical scientists, clinicians, and data scientists. Required Qualifications PhD in Electrical Engineering, Computer Engineering, Biomedical Engineering, Applied Physics , or related field. Demonstrated expertise in radar systems , RF engineering , signal processing , or physiologic signal analysis . Strong programming skills in Python , MATLAB , and/or C/C++ . Experience with mmWave radar (e.g., TI IWR series), UWB, or FMCW systems . Solid understanding of cardiopulmonary physiology and mechanical signatures of heart and lung activity. Evidence of strong publication record and independent research capability. Preferred Qualifications Experience with deep learning (CNNs, LSTMs, Transformers) for time‑series or radar data. Hands‑on experience with embedded platforms (ARM Cortex‑