Vacancies & Student projects

Open vacancies

There are no vacancies at the moment.

Student projects

We welcome good master's and bachelor's students to perform academic research in our group. We offer various projects that can be tuned to match your thesis requirements. You can also browse through our research pages to read about the different research topics of our group.

Using AI to improve vaccine safety assessment

Weakly supervised learning for automated scoring of the Monkey Neurovirulence Test (MNVT) in polio vaccine development

More information

Closed vacancies

The positions below are closed, please do not apply. They are listed to give you an idea of the kind of positions we regularly offer.

Vacancies

Postdoc Artificial Intelligence for Improving Toxicological Pathology

Data Manager Computational Pathology Group

Postdoctoral Researcher in Computational Pathology

Development and validation of machine-learning based histopathologic skin cancer diagnostics for real-world clinical practice

PhD candidate ‘AI-based grading of cancer for improved assessment of patient prognosis’

PhD candidate ‘Bridging the gap between AI development and clinical practice for histopathological skin cancer diagnostics’

PhD candidate ‘Development of explainable AI methods for end-to-end-learning with gigapixel images in the BigPicture consortium’

PhD candidate for Computational Pathology Research

PhD position on artificial intelligence for digital pathology and genetics in the gut microbiome

PhD Candidate for AI-based prostate cancer assessment at CWZ and Radboudumc

Crossing the boundaries: machine learning for pancreatic cancer across radiology, pathology and genetics (PANCAIM)

PhD candidate ‘Improving care for prostate cancer patient through AI-driven radiology/pathology fusion’

Closing the gap: Implementation of AI-based breast and prostate cancer grading algorithms in clinical practice

PhD candidate 'Towards a virtual pathologist via multi-modal foundation models for computational pathology'

PhD candidate 'Artificial Intelligence to improve detection and treatment of endometrial cancer via computational pathology'

PhD candidate AI-based pan-cancer prediction of biomarkers from histopathology

Postdoc Computational Pathology research

Postdoc 'Deep learning in digital gastrointestinal pathology'

Postdoc 'Deep learning to improve pathology diagnostics'

Postdoctoral Researcher for Deep Learning in Computational Pathology

Research Software Engineer in Digital Pathology

Student assistant Computational Pathology

Postdoctoral researchers in two projects on computational pathology

Student projects

Deep Learning to predict recurrence in triple negative breast cancer (TNBC)

Predicting Basal Cell Carcinoma Progression from Histopathology Using Artificial Intelligence

Deep Learning to detect artifacts in whole slide images

Deep Learning for the differentiation of STIC lesions

AI to analyze lung cancer histology images

Explainability through Reasoning Segmentation

Advancing Macrophage Biomarkers for Improved Immunotherapy Response Prediction

Automation of cytology-to-diagnosis using a smartphone

Artificial Intelligence-Based Analysis of Patient-Specific Characteristics in Human Bone Histology

Artificial Intelligence-Based Quantification of Spatial Organization in Human Bone Histology

Peritubular Capillary Segmentation in Kidney Transplant Biopsies

Tissue Segmentation in Kidney Transplant Biopsies

Multimodal Registration in Prostate Cancer

Artificial intelligence to integrate histopathological images and transcriptomics

Artificial Intelligence-Based Segmentation of Mononuclear Cell Infiltrates in Preclinical Toxicologic Pathology

Artificial Intelligence-Based Liver Hypertrophy Detection in Drug Safety Studies

An AI model to detect kidney abnormalities from drug safety testing

A comparative multi-modal AI model for liver hypertrophy detection in drug safety slides

Automated detection of DRESS syndrome

Predict neoadjuvant chemotherapy response in breast cancer histopathology from a panel of immunohistochemical markers

Identification of features in benign breast disease biopsies that predict breast cancer risk

Combining pathological features to predict pancreatic ductal adenocarcinoma survival

Unmix multiplex immunohistochemistry with deep learning

Improving PD-L1 digital pathology image analysis

Automated whole-slide image analysis and quality control

Self-supervised pretraining for digital pathology

T&T: When TILs meet TLS in NSCLC