We are seeking a highly motivated and talented individual to join our new team as a PhD-candidate at the CARA Lab, a multi-disciplinary and multi-institutional collaboration between Radboudumc and Amsterdam UMC.
The CARA Lab is a dynamic and innovative research group focused on the integration of advanced medical imaging and artificial intelligence technologies to improve diagnosis and treatment of cardiovascular diseases, especially coronary artery disease. The lab is a collaboration between Radboudumc, Amsterdam UMC and Abbott, a world-leading international medical device company. This lab is part of the 10-year LTP ROBUST program 'Trustworthy AI-based Systems for Sustainable Growth' consortium, which unites 17 knowledge institutions, 19 participating industry sponsors and 15 civil-social organisations from across the Netherlands. You will gain valuable experience working with an industry partner, and will be able to tap into a wealth of networking, career development, and training opportunities in conjunction with ICAI, the Innovation Center for Artificial Intelligence at the University of Amsterdam.
As a PhD candidate you will be involved in integrating novel AI algorithms for real-time analysis of optical coherence tomography (OCT) images of the coronary arteries, acquired during cardiac catheterization procedures. Collaborating with a multidisciplinary team, you will actively work on bridging the gap between AI technology and clinical practice. This will involve enhancing the output of AI models by focusing on aspects such as interpretability, reliability and trustworthiness. Additionally, you will conduct qualitative research to gain valuable insights into the perspectives of patients and operators, thereby promoting a comprehensive understanding of their perceptions and needs. Ultimately, you will contribute in the setup and implementation of a randomized clinical trial, specifically designed to evaluate the effectiveness and impact of real-time AI-based clinical decision-making during cardiac procedures.
You will work closely within a multidisciplinary team of clinicians, social science researchers, engineers, and industrial partners, and will have the opportunity to contribute to the development of state-of-the-art technology with the potential to transform the field of cardiovascular imaging. Mentoring junior team members and actively shaping the lab's research direction will be integral parts of the role. The position will be primarily based at the Radboudumc, with occasional visits to Amsterdam.
As a PhD you will:
Engage in research within a multidisciplinary group dedicated to developing AI algorithms for real-time analysis and interpretation of optical coherence tomography (OCT) images during cardiac procedures. Translate state-of-the-art technology into clinical practice, contributing to the successful implementation and output of the developed algorithms. Conduct qualitative research to gain insights into the perspectives of patients and operators, fostering a deeper understanding of their vision and incorporating it into the research outcomes. Design of a randomized trial focused on AI-based clinical decision-making on the cardiac catheterization lab, working on its setup and implementation. Disseminate research findings by publishing in esteemed peer-reviewed journals and presenting at international conferences. Provide mentorship to junior team members, actively contributing to their professional growth, while also playing a role in shaping the lab's research direction.
You should be a creative and enthusiastic researcher with an MSc degree in a social science discipline, such as (Applied) Ethics, Health Sciences, Humanities, Sociology, Science and Technology Studies, a technical-medical field such as Technical Medicine, or a related area. Candidates with an MD degree are also encouraged to apply.
You demonstrate a clear interest in the intersection of social sciences and technology, particularly in the context of healthcare and AI, showcasing a genuine passion for exploring the social and ethical implications of integrating advanced technologies in medical practice. Have expertise on, or affinity with, Human Computer Interaction in AI fields is a plus. You also have good communication skills and enjoy working in a multidisciplinary team.
Terms of employment
Preferred starting date is Q4 2023. The salary depends on education. Scale 10 will be offered to MSc medical graduates; those holding degrees in other disciplines will be offered scale 10A.
Working at Radboud university medical center means that you are ahead of the curve and working together on the healthcare of the future. And there is more. Our secondary terms of employment are impressive. These are fully tailored to you thanks to our Employment Conditions Selection Model. At Radboud university medical center, you will be given trust, and you will take the responsibility to handle everything together. We provide annual courses, both professional and personal.
A gross monthly salary between € 2.789 and € 3.536 (scale 10A) or between € 3.230 and € 5.088 (scale 10) based on full-time employment.
From 1 November 2023 the wages will increase by 4%. An annual vacation allowance of 8% and an end-of-year bonus of 8.3%. If you work irregular hours, you will receive an allowance. As a full-time employee (36 hours per week), you are entitled to approximately 168 vacation hours (over 23 days) per year. Radboud university medical center pays 70% of the pension premium. You pay the rest of the premium with your gross salary. *You get a discount on health insurance as well: you can take advantage of two group health insurance plans. UMC Zorgverzekering and CZ collectief.
You will work in the Diagnostic Image Analysis Group (DIAG). DIAG develops computer algorithms to interpret and process medical images. The group currently consists of around 70 researchers. Radboud University Medical Center and Radboud University are located in Nijmegen, the oldest Dutch city with a rich history and one of the liveliest city centers in the Netherlands.
Please apply here. You should supply a motivation letter, your CV, links to a Google Scholar profile, a list of grades and courses you have followed including online courses on deep learning and similar topics, and links to any publications you have written plus any code you have written and is publicly accessible, e.g., on a GitHub account. Applications are processed immediately upon receipt.