I feel there is tremendous potential for technological innovation to positively impact medical decision making. In my academic research, I want to explore that potential to support healthcare professionals and patients to make better decisions.
My main (“traditional”) research focuses on cardiac electrophysiology, in particular, using innovative tools and experiments to better understand sudden cardiac arrest. I am experienced in designing and executing in-silico, preclinical and clinical studies. My current focus is on using electrical and structural imaging to better understand why patients who have had a myocardial infarction later in life may develop a life-threatening arrhythmia, and how we can better predict who is at risk.
Additionally, I am currently exploring the value of generative AI for healthcare at large. I am interested how we can leverage recent innovations in AI for improving workflows for healthcare professionals (can we help reduce some of those energy-draining administrative tasks?) as well as empower patients. And which new mechanistic insights could we extract with these new techniques? I discuss my explorations in my newsletter, but am also exploring these topics in my academic work.
Vacancies and internships
Please find a list of current and upcoming vacancies below, as well as (unpaid) internships opportunities.
PhD position on exploring the electrical substrate for arrhythmias after myocardial infarction (position opening soon)
This vacancy is going to be opened soon. At Maastricht University, I’m seeking a PhD candidate to join our ELECSUR project, focusing on cardiac electrical substrate characterization in post-myocardial infarction patients. You’ll work with electrocardiographic imaging (ECGI), advanced ECG analysis and AI to improve risk prediction for ventricular arrhythmias. This research aims to understand how such arrhythmias develop after infarction, potentially reducing unnecessary ICD implantations and improving patient care.
Postdoc position on interpreting and simulating the electrical substrate for arrhythmias after myocardial infarction (position to be opened in the future)
For the project mentioned above, a postdoc position will be opened in due time.
Internships on cardiac electrophysiology
Using state-of-the-art technology, we collect electrical and structural data in patients at risk for sudden cardiac arrest. Such data include high-resolution electrical mapping data, imaging data, experimental data and computer simulations. I have a variety of topics that tech-minded students may find interesting to explore, with a focus on signal processing that leads to novel insights in cardiac electrophysiology. Contact me for more information! Concrete topics include:
- Can we use ECGI to understand normal and pathological variation in cardiac electrophysiology? How does this differ between sexes, and over time?
- What are normal and abnormal dynamics in activation and recovery of the heart? Can we see if abnormal dynamics leads to serious arrhythmias?
- How can we use hybrids of complex (ECGI) and simplified (ECG) technology, to learn from the first but detect with the latter?
Internships on the use of generative AI in healthcare
Generative AI holds a lot of promise, but its impact now depends on smart applications. Can we use it to improve the workflow of healthcare professionals, or increase our understanding of disease mechanisms? For example, I am exploring how large-language models (LLMs) may help us analyze large set of electronic health record notes, how we can help physicians deal with such data, and how we can increase our disease knowledge from that. Feel free to contact me if you would like to join me in exploring how AI may change healthcare, through existing projects or ideas of your own. Some ideas include:
- Using large (public) databases that include clinical notes from an EHR, how can we leverage LLMs to build a ‘knowledge database’ that connects symptoms, tests, diagnosis and therapy?
- Surveying clinicians, what is seen as the largest opportunity for GenAI in healthcare?
- Can we use LLMs for simulated patient trainings to help students prepare for medical consultation?