Blood Pressure Estimation from PPG Signals

Looking for an ML engineer to develop and implement a Model for real-time blood pressure (BP) estimation using heart rate photoplethysmograph (PPG) signals which will be deployed on an MCU for non-invasive health monitoring. Responsibilities: • Design and implement a robust algorithm for extracting BP values from raw or pre-processed PPG data (depending on the provided format). • Utilise machine learning or neural network techniques to achieve accurate BP estimation. • Optimise the algorithm for real-time performance on Nordic chipsets in an embedded C environment (or provide an embedded C based implementation of the model) • Integrate the algorithm seamlessly with existing hardware and software platforms (will be done by our engineers) • Conduct extensive testing and validation of the algorithm's accuracy and performance under various conditions. (will be done by our engineers) • Document the algorithm design, implementation, and testing process clearly and concisely. Ideal candidate: • Strong foundation in Python, C, C++ • Solid understanding of signal processing and machine learning/neural network techniques. • Experience with real-time data processing and embedded system optimization. • Familiarity with physiological signals, particularly PPG and blood pressure dynamics, is a plus. • Excellent analytical and problem-solving skills with a meticulous approach to detail. • Strong communication and documentation skills. References: (link removed)