Research and development of new algorithms for processing and classifying physiological signals in ambulatory systems
Work description
Processing of physiological and inertial signals (pre-processing, filtering, feature extraction in the time, frequency, and time-frequency domains). Development and validation of machine learning and deep learning models; integration and analysis of data from wearable and clinical monitoring devices and clinical databases. Experimental evaluation of algorithms, development, and deployment. Support in data collection and documentation of the work performed.
Academic Qualifications
Master's degree in Biomedical Engineering, Electrical Engineering, Computer Science, or a similar field.
Minimum profile required
Experience in biomedical signal processing.Knowledge of machine learning/deep learning (e.g., classification, feature learning, neural networks).Experience in scientific programming (e.g., Python and/or MATLAB) and code management tools.Good knowledge of written and spoken scientific English.
Preference factors
Previous work in developing algorithms for signal processing and machine learning/deep learning techniques with physiological signals, namely ECG and inertial from human movement. Previous knowledge in collecting physiological data and managing and preparing it for analysis.
Application Period
Since 08 Jan 2026 to 21 Jan 2026
Centre
Biomedical Engineering Research