Research and development of new algorithms for processing and classifying physiological signals in ambulatory systems
[Open soon]
Work description
Processing of physiological signals (pre-processing, filtering, feature extraction in the time, frequency, and time-frequency domains). Development and validation of machine learning and deep learning models for physiological signal analysis, with a focus on cardiology. 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 work performed in reports and scientific papers. Support in project management and new applications, as well as monitoring group members in related R&D lines.
Academic Qualifications
PhD 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.Knowledge of project management and application of Agile methodologies.
Preference factors
Previous work in developing algorithms for signal processing and machine learning/deep learning techniques with physiological signals, namely ECG. Previous knowledge in collecting physiological data and managing and preparing it for analysis.
Application Period
Since 12 Jan 2026 to 23 Jan 2026
[Open soon]
Centre
Biomedical Engineering Research