Postdoctoral researcher on the project “Artificial Intelligence-Driven Molecular

Current guideline-recommended risk stratification in high-risk non-muscle invasive bladder cancer (HR-NMIBC) is inadequate at predicting which patients are at the highest risk of developing progression to aggressive disease. In case of progression no personalized bladder-sparing treatment options are available for these patients and the bladder is surgically removed with a decrease of the patients’ quality of life. Clinically relevant RNA-based molecular subtypes have the potential to improve risk stratification by identification of patients at the highest risk of progression, but sequencing is costly and time-consuming.

Within AI-PRECISE we will use a multimodal approach by combining artificial intelligence (AI), histopathology images and RNA sequencing data to predict molecular subtypes in bladder cancer and to identify targets for novel bladder-sparing treatment options. The AI based targets for novel treatments will be validated in bladder cancer organoids developed in our group. The final aim of this project is creating trustworthy AI models to aid clinicians in accurate patient risk stratification followed by organoid-guided decision making.

The current project funded by NWO (Vidi: https://www.nwo.nl/en/researchprogrammes/nwo-talent-programme/projects-vidi/vidi-2023) is a continuation of the European CLARIFY project (https://cvblab.webs.upv.es/clarify-project.eu/) and continues this research with a postdoc, PhD student and lab technician.

As a postdoc you will focus on further developing and validating existing deep learning models for prediction of molecular subtypes on real-world HR-NMIBC cohorts. This will involve research into the use of state-of-the-art weakly-supervised and explainable AI techniques.

The postdoc will supervise a PhD student, will manage the project, plan, execute and troubleshoot experiments in a timely manner. There will be regular meetings with the principal investigators of the project and update on project progress. The postdoc is expected to write progress reports and other documents needed for the project. The candidate will keep up-to-date with the literature, write manuscripts, present the data at departmental meetings and scientific conferences and write follow-up grant applications for project continuation.

You should be an independent and highly motivated researcher with a PhD degree in the fields of Computer Science, Computer Engineering, Biomedical Engineering or similar with experience in (medical) image analysis and/or machine learning.

Affinity or experience with biomedical research (sequencing techniques, hisopathology) is highly valued.

You should be able to communicate and work with clinicians as well as researchers from various fields.

Excellent proficiency in English is required.

You will be based at the Department of Urology and the Department of Pathology and Clinical Bioinformatics at Erasmus MC in Rotterdam, as well as the Computational Pathology Group in the Pathology Department at Radboud UMC in Nijmegen. The Urology Department at Erasmus MC is a leading center for bladder cancer treatment, recognized both nationally and internationally. The department has an excellent track record in clinical care and research, utilizing cutting-edge multi-omics approaches and organoid techniques. The Department of Pathology and Clinical Bioinformatics specializes in analyzing clinical data, sequencing results, and pathology imaging. Its research covers a wide range of cancers, including triple-negative breast cancer, lung neuroendocrine tumors, uveal melanoma, pancreatic cancer, bladder cancer, skin cancer, soft tissue sarcomas, colorectal cancer, tongue squamous cell carcinoma, and more, with a focus on developing AI models that enhance tumor diagnosis, identify molecular subtypes, and improve predictions of prognosis and treatment responses. Both departments are internationally renowned for their high-impact research and provide an inclusive, collaborative, and supportive working environment. Erasmus MC offers state-of-the-art research facilities, including a GPU cluster, dedicated data servers, and a personal GPU-enabled computer for your work. Similar advanced infrastructure is also available at Radboud UMC.

We do not discriminate on the basis of sex, gender, belief, culture, place of birth or occupational impairment when recruiting and selecting staff and students. You will collaborate with a team of clinicians (urologists/pathologists), experienced researchers with backgrounds in image analysis, machine learning, pathology, surgery, and oncology.

The project is performed in collaboration with RadboudUMC, Johns Hopkins University, ProBCI (Dutch Bladder Cancer Infrastructure) and IKNL (Netherlands Comprehensive Cancer Organisation). Supervision for the projects will be provided by assistant professor Tahlita Zuiverloon (MD PhD), assistant professor Martijn Starmans (PhD), assistant professor Farhan Akram (PhD) all Erasmus MC and professor Geert Litjes (PhD) from Radboud UMC.

For more information about this position, please contact Tahlita Zuiverloon, e-mail: t.zuiverloon@erasmusmc.nl.