PhD - Medical image analysis and AI in hemostasis reseach

Would you like to use artificial intelligence to advance medical research and diagnostics? In this innovative research project at Maastricht University, you will develop AI solutions that automate the analysis of microscopic images and contribute to improving the diagnosis of bleeding and coagulation disorders. You will work at the intersection of biomedical science, data science, and clinical applications.

PhD candidate – Medical image analysis and AI in hemostasis reseach

• Our goal: to develop an automated, high-throughput analysis pipeline for flow chamber images using artificial intelligence. By doing so, we aim to accelerate, standardize, and further improve the analysis of thrombus formation under flow conditions for both scientific research and future clinical applications. The generated data will be used to study clinically relevant hemostatic phenotypes, treatment effects, and the potential value of flow chamber testing in patients with bleeding disorders.
• Your colleagues: you will become part of the Department of Biochemistry within the Faculty of Health, Medicine and Life Sciences (FHML), the Cardiovascular Research Institute Maastricht (CARIM), and work closely with the Central Diagnostic Laboratory (CDL) of Maastricht UMC+. You will join a multidisciplinary team of researchers, laboratory analysts, clinical chemists, hematologists, data scientists, and IT specialists.

You will play a central role in developing AI methods for the automated analysis of images generated by the Maastricht Flow Chamber. This will be the primary focus of your research. The Maastricht Flow Chamber is a technology that enables real-time visualization and analysis of thrombus formation under physiological flow conditions. Curious to learn more about this technique? Watch the short video, “Let it flow: what flowing blood is telling us about blood clotting”:
.You will also apply similar AI- and data-driven approaches to other biomedical datasets, contributing to a broader research programme covering methodological development, medication studies, and clinical translation, as well as to progress reports and scientific publications.

What you do

• Analyze the current (semi-)automated image and data analysis workflow of the Maastricht Flow Chamber and identify opportunities for improvement.
• Develop AI models for the automated segmentation, quantification, and classification of flow chamber images, using deep learning and self-supervised techniques that also leverage unlabeled data.
• Automate the processing and analysis of large datasets generated from flow chamber experiments.
• Validate the developed AI models and translate them into a robust high-throughput analysis method that aligns with clinical questions in hematology, such as the diagnosis of unexplained bleeding and clotting disorders.
• Depending on the progress of the project, apply similar data science and AI methods to other complex laboratory datasets in the field of hemostasis and thrombosis.
• Collaborate closely with laboratory researchers, clinicians, and data scientists to integrate imaging results with clinical and laboratory data and help translate these findings into clinically relevant conclusions.
• Present your findings at national and international conferences and publish your work in leading international scientific journals.

What you bring
At Maastricht University, we believe that talent comes in many forms. We are looking for a curious researcher with a strong affinity for artificial intelligence, medical image analysis, and data science, who enjoys bridging computational methods and clinical applications. Do you recognize yourself in the following?

• You hold a completed Master's degree, preferably in biomedical engineering, biomedical sciences, medicine, systems biology, clinical health sciences, (clinical) data science, bioinformatics, or a related discipline.
• You have a demonstrated affinity for data analysis and programming (e.g. Python or R), e.g. via projects, internships or other work experience where you’ve analysed datasets. Experience with machine learning, deep learning (e.g. scikit-learn, Tensor Flow or PyTorch), or medical image analysis (e.g.…