MSAI LABS

Compensation

Full Description

This is a full-time remote role for a Research Associate in Signal Processing and Physiological Modeling, specializing in HD5y files extracted from hospital ICU machines. The incumbent will lead advanced research in signal processing and physical body flow modeling, with a focus on trauma patient data. Responsibilities include developing and implementing algorithms that link ECG and PPG signals to circulatory and systemic flow dynamics, particularly under acute stress conditions. This is a government-sponsored Research Associate role, offering the unique opportunity to contribute to federally backed trauma research. Unlike equivalent university posts, this position can also provide stock options as an incentive, aligning long-term rewards with the growth of MSAI LABS. Day-to-day tasks involve: • Programming and algorithm development for physiological signal interpretation • Applying pattern recognition and denoising techniques to trauma-linked biosignals • Modeling circulatory flow and systemic responses using multi-modal data • Collaborating with cross-functional teams to advance the lab’s trauma-focused AI research objectives Qualifications • Strong foundation in Python and/or R • Experience modeling physical body flow dynamics using biosignals (ECG, PPG, BP, etc.) • Familiarity with trauma physiology and acute care signal patterns • Optimization skills for running models on cloud GPU/CPU instances • Knowledge of autoencoders, STFFT, SSD architectures or other denoising methods • Time series Transformers optimization • Expertise in pattern recognition and physiological signal interpretation • Proficient in programming languages relevant to signal processing and modeling • Solid understanding of mathematics, especially as applied to dynamic systems and signal flow • Strong analytical and problem-solving skills • Ability to work independently and remotely • Excellent written and verbal communication skills • Experience in a research environment is a plus • Master’s or Ph.D. in a related field such as Biomedical Engineering, Computer Science, Electrical Engineering, or Applied Mathematics