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Postdoctoral Position – Machine Learning for the Acceleration of Simulations
University Hospital Schleswig-Holstein. Develop data-driven surrogate models to replace computationally expensive CFD and FEM simulations for aneurysm treatment .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing data-driven surrogate models and machine learning pipelines for scientific applications, particularly in the context of computational fluid dynamics (CFD) and finite element methods (FEM). Proficient in integrating models into in silico trial frameworks and collaborating with multidisciplinary teams to validate simulation techniques.
Highest-signal resume keywords
PhD In Computer ScienceMachine Learning For Scientific ApplicationsProficiency In PythonCFD And FEM FamiliarityHigh-Performance Computing Experience
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
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Hard Skills
Surrogate ModelingReduced-Order ModelsPhysics-Informed Machine LearningModel EvaluationData-Driven Modeling
Soft Skills
CollaborationScientific CommunicationLeadership
Tools & Technologies
PyTorchTensorFlowJAX
Industry Keywords
Computational Fluid DynamicsFinite Element MethodsIn Silico TrialsAI-Based MethodsDevice Optimization
Tech Stack
Tools & technologiesPythonPyTorchTensorflow
About the role
Key responsibilities & impact- 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
Requirements
What you’ll need- 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)
- Proficiency in Python and relevant ML frameworks such as PyTorch, TensorFlow, or JAX
- Familiarity with CFD, FEM, or other numerical simulation methods is a significant advantage
- Experience with high-performance computing (HPC) environments is beneficial
- Submit a motivation letter (max. 1 page) and CV (max. 2 pages), including degree certificates and job references where applicable, as a single PDF by 27 October 2026
Benefits
Comp & perks- Salary based on the German E13 TV-L scale (100%), if terms and conditions under collective bargaining law are fulfilled
- Full-time employment, currently 38.5 hours/week; part-time employment may be possible within certain working time models
- Flexible working hours to accommodate individual needs
- Interdisciplinary research at the intersection of medicine, physics, engineering, and computer science
- Vibrant, international biomedical research group embedded within a university hospital setting