Postdoctoral researcher for novel federated learning approaches and models for diagnostic pathway support and risk score prediction in cardiology using real-world data

We offer an exciting post-doc position at the Universitat de Barcelona to develop fair, unbiased and privacy-preserving federated learning methods and algorithms for diagnostic pathway support and risk score prediction to assist cardiologists at the emergency and outpatient units using real-world Electronic Health Records data. Technical aspects of the project include federated learning, machine learning, deep learning, and AI interpretability and uncertainty. You will be joining the Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), a young and dynamic research group, aiming to enhance medical care through big data-enabled AI. We are seeking candidates with a PhD degree in an area pertinent to the project, such as applied mathematics, statistics, machine learning, data science, medical imaging, and/or biomedical informatics. We are looking for highly motivated candidates with strong interests in mathematical and computational applications in biomedicine. Candidates must have excellent teamwork and communication skills and be enthusiastic about their research. Due to multiple collaborations within the DataTools4Heart project with consortium partners across Europe, advanced oral and writing English knowledge are required. Female applicants are explicitly encouraged to apply.

DataTools4Heart (DT4H) is a new large-scale international project funded by the European Commission to build an efficient and unbiased privacy-preserving federated learning environment that will enhance quality, interoperability and re-usability of cardiology data across Europe. This 4-year research project will start on 1st October 2022 and comprises a consortium of 16 partners, including European world-renowned research institutions, companies and clinical centres of excellence, and the European Society of Cardiology. DT4H will improve the delivery of care and advance cross-border research in cardiology by allowing scientists and clinicians to harness the full potential of real-world health data, including currently inaccessible unstructured data. To this end, it will offer advanced solutions to cope with data heterogeneity across European regions and cardiology units, and to enable multi-site federated data use. Together with its toolbox, DT4H will leave the legacy of a federated learning platform with an embedded metadata catalogue and AI virtual assistants, and an open database of synthetic cardiac data remaining available for further research and AI experimentation.

The University of Barcelona (UB), founded in 1450, is one of the oldest universities in Spain. It comprises a student body of 84,370 and 4,548 research staff members. With 73 undergraduate programs, 273 graduate programs and 48 doctorate programs, UB is the largest university in Barcelona and Catalonia. The UB is ranked the first Spanish university according to several rankings (QS World University Rankings 2018, ARWU/Shanghai Ranking 2018). It is particularly interested in fostering international relations and, for many years, has managed an average of 150 European projects per year. Since January 2010, Universitat de Barcelona is part of the prestigious League of European Universities Research (LERU). The selected candidate will join the Artificial Intelligence in Medicine Lab at the University of Barcelona (BCN-AIM), a young and dynamic research group that is aiming to develop the next-generation of technologies that will improve medicine and health through big data-enabled AI.

How to apply; https://seu.ub.edu/ofertaPublicaCategoriaPublic/listPublicacionsAmbCate…