Are you interested in the cross-section of science and machine learning applied to heatlth care? Join the Data Science Methods lab at the UMC Utrecht and collaborate on cutting edge research for electrocardiography (ECG) analysis.
This PhD position is part of the SciML4Medicine project, funded by Health Holland and co-funded by industrial partners. SciML4Medicine brings together experts in `convential’ ECG analysis and in machine learning and aims to yield both novel insights relevant to electrocardiography, and produce ECG-AI models for clinical use cases that are more robust and efficient by grounding the AI models in cardioelectrophysiology.
The PhD student will:
- Create up to date overview of published SciML work in medicine.
- Improve long standing problems in ECG-analysis (forward problem and backward problem) with machine learning techniques.
- Build generate AI models for synthetic ECGs, exploiting electrophysiological knowledge.
- Develop robust and efficient deep learning models for clinically relevant tasks (e.g. medical diagnosis, prognosis predictions), levaraging ECG-domain knowledge.
You will work together in a diverse team of excellent researchers in the field Data Science at the Julius Center, collaborating closely with experts from the cardiology department.
SciML4Health background:
Electrocardiograms (ECGs) are a cornerstone of cardiologic practice. The current approach to analyzing ECGs relies on bespoke mathematical models. Now AI methods are revolutionizing health care, but these AI models are data-hungry. SciML4Medicine brings out the best of both worlds by designing AI models informed by mathematical models of ECGs.