Postdoc Researcher AI/Machine Learn for Multimodal Cardiovasc Data (VASCUL-AID)

Are you a machine-learning researcher who wants your models to change how patients are treated? Do you enjoy turning complex, multimodal clinical and molecular data into predictions that clinicians can trust? Then join the Dep of Vasc Surg as the AI/ML postdoc of the European VASCUL-AID consortium. VASCUL-AID is a €6.3 million Horizon Europe project (2023–2029), coordinated by Amsterdam UMC, that brings together 16 partner organizations across Europe. Its goal is a trustworthy AI platform that predicts cardiovascular risk and disease progression in patients with abdominal aortic aneurysm (AAA) and peripheral artery disease (PAD). Both conditions are common, often silent until late, and carry a high risk of major cardiovascular events, amputation and death. A key challenge in treating these patients is that it is still unclear which risk factors drive disease progression and the development of other cardiovascular conditions. The consortium therefore collects and integrates rich longitudinal patient data from major European clinical centres: electronic health records, proteomics, genomics, imaging (CT/MRI), wearable sensor data and clinical follow-up. These data form the basis for models that support personalized risk assessment and treatment decisions. More information about the project: www.vascul-aid.eu. As a postdoctoral researcher, you will lead the AI/machine-learning and multimodal data-integration work of VASCUL-AID at Amsterdam UMC. You will design, build and validate models that predict disease progression and clinical outcomes in AAA and PAD patients, using the full breadth of the consortium's data. Your work spans the whole modelling pipeline: from harmonizing heterogeneous clinical and molecular data, to developing graph-based and foundation-model approaches, to delivering robust, uncertainty-aware models that clinicians can trust and use. You will also drive data-driven biomarker discovery together with biomedical and clinical colleagues. You will beresponsible for governance of the data data, handling data (harmonization, missing data, synthetic data), coordinating data collection together with the team. You will be helping in supervising the consortium tasks and PhD-_tudents working on prediction models. You will be embedded in the Department of Vascular Surgery and work closely with vascular surgeons, clinical researchers, data scientists and partner institutions across Europe. This position offers a unique opportunity to translate advanced machine learning and multi-modal prediction models into real clinical impact, in a highly visible international consortium. Lead the development of machine-learning models/multi-model prediction models that predict cardiovascular disease progression and outcomes in AAA and PAD patients. Integrate multimodal patient data: EHR, proteomics, genomics, imaging, wearables and clinical data into coherent analysis pipelines and representations. Apply and advance state-of-the-art methods, including graph representation learning (e.g. graph neural networks), foundation-model embeddings, and multi-omics integration. Develop uncertainty-aware models and rigorous validation strategies (internal and external validation, calibration, robustness across cohorts). Contribute to biomarker discovery by identifying predictive molecular and clinical signatures, in collaboration with biomedical and clinical partners. Prepare models for deployment: reproducible code, documentation, model packaging, and integration with consortium tools and clinical workflows. Coordinate and governance of the data collection, handling of data (work with missing data, data harmonization process and creating synthetic data) Coordinate the AI/ML work package activities at Amsterdam UMC and act as the technical point of contact for consortium partners. Supervise and support PhD candidates, master _tudents, and junior data scientists. Publish in high-impact journals and conferences, present at international meeting