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Postdoc Position Data Science and Artificial Intelligence in Cardiac Electrophysiology Welcome to Maastricht University! You just finished your PhD trajectory and looking for the next step in your academic career? Your interests lie in the field of machine learning techniques, particularly artificial neural networks, and deep learning? And you would like to continue your research on clinically relevant topics? If yes, then Maastricht University has a new challenge for you! Postdoc Position Data Science and Artificial Intelligence in Cardiac Electrophysiology - Our goal: the development of multimodal prediction models to detect and predict atrial fibrillation (AF) and other clinically relevant cardiac rhythm patterns, predict disease progression and treatment response, and support personalised AF management. - Your colleagues: The Department of Physiology at Maastricht University is a key academic partner in AF-B-STEP, contributing expertise in AF burden quantification, AI-based rhythm analysis, and digital biomarkers. The department has a strong data science environment, with an additional team of three to four data scientists working across related cardiovascular research projects. As part of the Innovative Health Initiative (IHI) project AF-B-STEP (Quantifying Atrial Fibrillation Burden to inform Screening, Treatment, and hEalth Policy, https://afbstep.eu /) , we aim to establish atrial fibrillation (AF) burden as a novel biomarker for precision medicine. AF-B-STEP brings together leading academic institutions, medical device manufacturers, pharmaceutical companies and wearable technology developers to integrate and analyse one of the world's largest collections of AF burden data, comprising more than 400,000 individuals and 200,000 patient-years of linked clinical outcome data. For this project, the Department of Physiology at the Cardiovascular Research Institute Maastricht (CARIM) has a vacancy for a postdoctoral researcher i...
How to apply
CV with attached letter,Online application form,Telephone
Contact: Ulrich Schotten | Tel: +31433882222 | Email: [email removed]
