PhD Position: AI-driven analysis of kidney biopsies for transplantation outcomes

Working at the UMCG means being part of an inspiring and professional environment where development and innovation are key. Within the ADORABLE consortium, funded by the Dutch Kidney Foundation and NWO, you will contribute to groundbreaking research that could transform the future of kidney transplantation.

Background

The ADORABLE consortium brings together academic and clinical partners to improve the selection and assessment of donor kidneys for transplantation. Each year, over 1,000 kidney transplants are performed in the Netherlands, half of which involve kidneys from deceased donors. The 10-year survival rate of kidneys from deceased donors is only 50%, compared to 70% for living donors. At the same time, many potentially usable kidneys are rejected, partly due to a lack of reliable predictors of transplantation outcomes.

The ADORABLE consortium aims to develop an advanced, data-driven assessment system for donor kidneys. Central to this is the use of machine learning to evaluate the predictive value of biomarkers from various sources: donor-related data, perfusion fluid, and kidney biopsies. Kidney biopsies may contain unique information about organ quality that is not visible in blood or clinical characteristics. By combining the results of AI-driven image analysis of histological samples conducted in this PhD project with biomarker data and outcome data, the consortium aims to develop a robust model that can better predict transplantation outcomes.

This innovative approach will contribute to more reliable donor kidney selection, reduced rejection rates, and improved long-term outcomes for patients. The project offers a unique opportunity to contribute to socially relevant research with direct clinical impact.

What will you do

As a PhD candidate, you will contribute to the development of an advanced prediction model for transplantation outcomes of donor kidneys. You will focus specifically on the analysis of pre-implantation kidney biopsies using deep learning and AI-driven image analysis. Your responsibilities will include:

• Analysing pre-implantation kidney biopsies according to the Banff criteria;
• Applying AI methods for automatic segmentation and morphometry of histological images;
• Comparing the predictive value of AI-driven image analysis with clinical and biomarker data;
• Collaborating with international experts in medical image analysis and pathology;
• Presenting results at (inter)national conferences and publishing in scientific journals.

Where will you work

You will work within the Department of Pathology & Medical Biology at the University Medical Center Groningen (UMCG), in a multidisciplinary team of pathologists, nephrologists, medical biologists, data scientists and AI experts. The project is part of the national ADORABLE consortium, which focuses on improving the selection and outcomes of donor kidneys. You will collaborate closely with other PhD candidates within the consortium.

What do we ask

We are looking for a motivated candidate with a strong sense of curiosity who has:

• A recently completed Master’s degree in Artificial Intelligence, Computer Science, Biomedical Engineering or Technical Medicine;
• Demonstrable experience with deep learning, medical image analysis and/or medical imaging;
• Excellent communication skills in English (written and spoken);
• Independence, initiative and strong organisational skills.

What do we offer

• A challenging PhD position for 4 years (36 hours per week);
• An inspiring working environment with room for personal development;
• Salary in accordance with the CAO UMC, scale PRO-0 (minimum € 3.108 and maximum € 3.939 gross per month for a full-time position);
• Excellent secondary employment conditions, including an end-of-year bonus of 8.3% and holiday allowance of 8%;
• The conditions of employment comply with the Collective Labour Agreement for Medical Centres (CAO-UMC).

Are you enthusiastic about this PhD position and would you like to contribute to better outcomes for kidney transplant patients?