PhD position: modeling menopause-related heart rhythm disorders

Menopause induces profound electrical and structural changes in the heart, yet the mechanisms driving this transition, and the reasons it dramatically increases the risk of heart rhythm disorders in women, remain largely unexplored. In this PhD project, you will play a central role in uncovering these mechanisms by developing advanced, multi-scale computational models of atrial electrophysiology.

You will work with one of the most comprehensive and unique datasets available worldwide: high-resolution intra-operative electrical mapping data recorded directly from the human atria during cardiac surgery, combined with ECG, PPG, hormone profiles, and continuous vital signs. All data is collected within Erasmus MC, giving you direct access to clinicians, engineers, and the surgical team collecting the data. This means you will not rely on synthetic or incomplete datasets but you will build models grounded in rich, real-world physiology.

Your task is to transform these multimodal datasets into patient-specific “digital twins” that simulate how atrial electrophysiology evolves across the menopausal transition. You will design and calibrate mechanistic models at the cellular, tissue, and organ scale; develop numerical methods and optimization pipelines; and run large-scale simulations to reconstruct remodeling trajectories and identify the critical “breaking points” that lead to arrhythmias such as sinus node dysfunction and atrial fibrillation. The mechanistic insights and model outputs you generate will directly feed into the women-specific arrhythmia detection algorithms developed by our industrial partner. Your work will therefore have a clear translational pathway toward real-world digital health solutions.

Beyond modeling, you will contribute to algorithm development for arrhythmia prediction and collaborate closely with clinical researchers during patient recruitment and data collection. This position is ideal for someone who enjoys combining computational modeling, physiology, and real clinical data. Your work will directly advance women-specific arrhythmia detection and care, making a scientific impact that is both academically significant and clinically meaningful.

“What I enjoy most is seeing how mathematical models can reveal mechanisms that cannot be directly observed from data.” - team member

The position will be available by Q4 2026. The project is funded by Health-Holland.