We are looking for a PhD candidate to develop AI models using MRI and ECG data to predict and improve outcomes of sudden cardiac death. The project is carried out in close collaboration between our qurAI group and clinical experts from the Department of Cardiology.

Sudden cardiac death (SCD) is a major healthcare problem worldwide, affecting millions of individuals annually.  Current prediction models for SCD have limited predictive ability, and result in both underdiagnosis and overtreatment of patients with a risk of SCD. Moreover, about ~50% of SCD victims has not been identified with heart disease or even cardiac symptoms. This urges the need for new methods to identify these patients at an earlier stage. Artificial intelligence provides new opportunities for personalized prediction of SCD, using multi-modal datasets. This project aims to take a next step towards prediction of SCD by using deep learning to analyze multi-modal patient data such as cardiac MRI and electrocardiograms (ECG).

What are you going to do

As part of your PhD, you will be involved with data preparation, design deep learning models and drive the roadmap toward clinical implementation. Specifically, you will address challenges associated with analysis of multi-modal and high-dimensional data.

The PhD project is embedded the qurAI group at the Department of Biomedical Engineering and Physics and the Department of Cardiology.

The qurAI group is an interfaculty, multidisciplinary group between the Department of Biomedical Engineering and Physics of the Amsterdam University Medical Center (AUMC) and the Institute of Informatics of the University of Amsterdam. The qurAI group focuses on the development, validation and clinical integration of AI solutions for data analysis challenges in healthcare. The group aims at designing and enabling socially responsible AI innovations in healthcare.

The research group at the Department of Cardiology is recognized worldwide for their pioneering research into innovative device treatments for patients with cardiac arrhythmias (including leadless pacemakers and subcutaneous ICDs). This group has a lot of experience with international, multicenter clinical research, as well as the ambition to implement new technologies such as AI in the clinic.

You will work under the supervision of Prof. Ivana Isgum from the qurAI and Dr. Fleur Tjong from the Department of Cardiology.

You are expected to:

• Complete and defend a PhD thesis within the official appointment duration of four years;
• Publish and present your work in journals and international conferences;
• Be an active part of the qurAI group and their activities as well as part of the Department of Cardiology;
• Assist in educational activities and supervision of bachelor and master students.

What do you have to offer

• A Master’s degree (or equivalent) in Biomedical Engineering, Artificial Intelligence, Computer Science, or a related subject;
• Previously demonstrated interest in machine learning, deep learning and medical image analysis, in the form of coursework, projects and academic publications, and an affinity with medical topics;
• Good programming skills, particularly in Python and ML-related libraries;
• High motivation in pursuing academic research;
• Fluency in English, both written and spoken.

Our offer

• A flying start to your career in research work in a metropolis with a diverse and open culture, and a multicultural society.
• Working on large-scale and in-house research, with motivated colleagues from all corners of the world.
• You will start with a contract for one year (12 months) in accordance with the CAO UMC 2022-2023, with the possibility of extension for another three years (36 months).
• An active staff association and the Young Amsterdam UMC association, both of which organize fun (sports) activities and events.

For more details, please take a look here.

Job application

Do you recognize yourself in the job profile? Then we look forward to receiving your application by 13 November 2022 via this link.

Applications should include the following information:

• A detailed CV including the months (not just years) when referring to your education and work experience;
• A letter of motivation.