Mireia Calvo González was born in Barcelona, Spain, in 1988. She obtained her MSc and BSc in Telecommunication Engineering from Universitat Politècnica de Catalunya – Barcelona Tech (UPC) in 2011; performing her Master’s Thesis at the Illinois Institute of Technology (IIT) in Chicago, USA.

In 2014, she obtained her MSc in Biomedical Engineering from both Universitat de Barcelona (UB) and Universitat Politècnica de Catalunya – Barcelona Tech (UPC). At the same time, she worked as a part-time eHealth researcher at Barcelona Digital Technology Center, and then as a full-time research engineer at Hospital Clínic de Barcelona.

In 2017, she obtained her European PhD in Signal Processing from Université de Rennes 1 (France), supervised by Alfredo Hernández from the Laboratoire du Traitement du Signal et de l’Image (LTSI), and in Biomedical Engineering from Universitat Politècnica de Catalunya – Barcelona Tech (UPC), supervised by Pedro Gomis from the Centre de Recerca en Enginyeria Biomèdica (CREB), thanks to a postgraduate grant of the Obra Social La Caixa.

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Then, she holded a postdoctoral position in the Laboratoire du Traitement du Signal et de l’Image, at Université de Rennes 1, in partnership with Cairdac, a company specialized in medical devices. Currently, she is a postdoctoral researcher at the Biomedical signal processing and interpretation department from the Institute for Bioengineering of Catalonia (IBEC), thanks to the Bioengineering Excellent Scientific Training (BEST) Programme, a postdoctoral fellowship part funded by the European Commission under Horizon 2020’s Marie Skłodowska-Curie Actions COFUND scheme and the Severo Ochoa programme of the Spanish Ministry of Science and Competitiveness. At the same time, she is part-time lecturer in the master of Data Science, at Universitat Oberta de Catalunya (UOC), as well as in the master of Biomedical Engineering, at Universidad Internacional de Valencia (VIU).

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Research interests

Her research interests are focused on biomedical signal processing, modeling and machine learning, applied to cardiovascular and respiratory diseases.

She has collaborated in several national and international projects with the aim of improving the understanding of cardiac diseases such as atrial fibrillation or the Brugada syndrome.

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