Postdoctoral Position (m/f/d) Machine Learning for the Accel

We are looking for professional and competent support to start as soon as possible , limited for 4 years.

What we offer:

• The salary will be based on the German E13 TV-L scale (100%), if terms and conditions under collective bargaining law are fulfilled
• A full-time employment, currently 38.5 hours/week; a part-time employment may be possible within the framework of certain working time models
• Flexible working hours to accommodate individual needs
• Interdisciplinary research at the intersection of medicine, physics, engineering, and computer science
• A vibrant, international biomedical research group embedded within a university hospital setting

Your tasks:

• Develop data-driven surrogate models to replace computationally expensive physics-based simulations for aneurysm treatment, namely, computational fluid dynamics (CFD) and finite element modeling (FEM), enabling rapid, on-demand simulation of patient-specific treatment scenarios
• Design and implement machine learning (ML) pipelines using full-fidelity CFD and FEM data, ensuring models generalize across patient geometries and device configurations
• Integrate trained surrogate models into the broader in silico trial framework and evaluate their accuracy, robustness, and computational efficiency against full-fidelity simulations
• Explore AI-based methods for treatment outcome prediction and device optimization , contributing to the framework\'s clinical decision-support capabilities
• Collaborate closely with clinicians, scientists, and engineers to develop and validate cutting-edge simulation techniques
• Lead and co-author scientific publications, and represent the project at international conferences

Your profile:

• PhD in Computer Science, Applied Mathematics, Physics, Engineering, or a related field with a strong computational focus
• Strong practical experience in machine learning for scientific applications — including surrogate modeling, reduced-order models (ROM), or physics-informed machine learning (PIML/PINNs) — is the core technical foundation of the role
• Proficiency in Python and relevant ML frameworks (e.g., PyTorch, TensorFlow, JAX) is essential
• Familiarity with CFD, FEM, or other numerical simulation methods is a significant advantage
• Experience with high-performance computing (HPC) environments is beneficial

Please submit a motivation letter and CV, including, where applicable, degree certificates and job references, as a single PDF until 22. June 2026 , indicating the reference number 28652 .

For questions about the role, please contact t

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Quelle: Statistisches Bundesamt (Destatis), Tabelle; dl-de/by-2-0; eigene Berechnung

Veröffentlicht am 01. Nov. 2026

Veröffentlicht am 01. Nov. 2026