University Hospital Schleswig-Holstein

Machine Learning Engineer

• Develop data-driven surrogate models to replace computationally expensive CFD and FEM simulations for aneurysm treatment
• Design and implement machine learning pipelines using full-fidelity CFD and FEM data
• Ensure models generalize across patient geometries and device configurations
• Integrate trained surrogate models into the in silico trial framework
• Evaluate model accuracy, robustness, and computational efficiency against full-fidelity simulations
• Explore AI-based methods for treatment outcome prediction and device optimization
• Collaborate with clinicians, scientists, and engineers to develop and validate simulation techniques
• Lead and co-author scientific publications
• Represent the project at international conferences

Explore Postdoctoral Position – Machine Learning for the Acceleration… at University Hospital…. Review requirements and application details.

Postdoctoral Position – Machine Learning for the Acceleration of Simulations