This PhD project is part of the European project “Computational Models for patient stratification in urologic cancers – Creating robust and trustworthy multimodal AI for health care – COMFORT” which aims to develop and prospectively validate multimodal, AI-based decision support tools to improve detection, diagnosis and prognosis of patients with prostate cancer (PCa) and kidney cancer (KC).
Prostate cancer (PCa) and kidney cancer (KC) are among the most prevalent cancers. Europe sees some of the highest incidence rates of KC for both men and women in the world, whereas PCa accounted for approximately 23 % of all new cancer cases diagnosed in men in European Union countries in 2020. These cancers have a significant impact on the health and quality of life of those affected and place an increasing burden on our healthcare system, with costs exceeding EUR 12 billion each year.
Modern medicine generates a tremendous amount of structured and unstructured data (such as electronic health records, biomarkers, and complex medical imaging) that surpasses human analytical abilities, resulting in inaccurate diagnoses and ineffective treatments. Efficiently leveraging health data can reduce these burdens, but current clinical methods fall short in utilising the wealth of available data.
To address these challenges, the newly launched COMFORT research project is developing a cutting-edge decision support system. The innovative tool will use artificial intelligence (AI) and data-driven insights to assist medical professionals in delivering improved care for people affected by PCa or KC.
The goal of this PhD project is to create AI-based models for PCa and KC which incorporate multi-modal complex health data from multiple sources such as image data, laboratory examinations, and unstructured medical reports.
Tasks and responsibilites
- Conduct research in the development of multimodal AI for urologic cancer
- Collaborate with a multidisciplinary team to translate cutting-edge technology into clinical practice
- Publish research findings in peer-reviewed journals and present at international conferences
- Mentor junior team members and contribute to the development of the lab's research direction
- be a creative and enthusiastic researcher with an MSc degree in Computer Science, Mathematics, Physics, Engineering or similar
- have a clear interest to develop artificial intelligence algorithms and an affinity with healthcare
- have good communication skills and enjoy working in a multidisciplinary team
- have expertise in software development, preferably in Python
- having experience with deep learning, machine learning, and image analysis is a plus.
Terms of employment
You will be appointed for four years as a PhD student with the standard salary and secondary conditions for PhD students in the Netherlands. The research should result in a PhD thesis.
You will work in the Diagnostic Image Analysis Group (DIAG) DIAG is part of the Departments of Imaging, Pathology, Ophthalmology, and Radiation Oncology of Radboud University Medical Center. We develop 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. Radboud University has over 17,000 students. Radboud UMC is a leading academic center for medical science, education and health care with over 8,500 staff and 3,000 students.
For more information please contact Alessa Hering by e-mail.
Use the Apply button on this page to apply for this position. You should supply - motivation letter - your CV including links to a Google Scholar profile - list of grades and courses you have followed including online courses on deep learning and similar topics, - links to any publications you have written - Any code you have written and is publicly accessible, e.g., on a GitHub account. This application will remain open until the position has been filled. Applications are processed immediately upon receipt.