Research Associate / PhD Student (f/m/d) - Development of methodologies for high fidelity digital twins targeting industry scale wind turbines
23.01., Wissenschaftliches Personal
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The position addresses critical challenges faced by modern wind turbines operating under changing climate conditions, shifting wind patterns, and structural ageing. As turbines experience evolving loads, discrepancies arise between physical assets and their nominal digital designs, complicating accurate prediction of structural behavior and sustainable lifecycle management. This research aims to overcome these challenges by advancing sensitivity-based modelling, fluid–structure interaction (FSI) methods, inverse problem solving, and surrogate modeling techniques, ultimately enabling predictive, adaptive, and efficient digital twin frameworks for real-world wind turbines.
Position information:
1. Application deadline: 30.04.
2. Starting date: 01.09.
3. Position type: full time
4. Position duration: 3 years
Research Objectives
5. Development of sensitivity framework for coupled sensitivity analysis.
6. Extend the developed framework to support FSI problems, and identify suitable sensitivity computation methods.
7. Identify important modelling parameters for the digital model.
8. Create a digital model of the wind turbine whilst having the important modelling parameters variable.
9. Develop methodologies to solve coupled inverse problems.
10. Use the measurement / test data to identify the high-fidelity modelling parameters by solving the inverse problem.
11. Validate the digital model against test scenarios.
12. Perform what-if analyses for the developed digital models.
13. Develop interfaces to provide feedback from the digital twin to the physical turbine.
14. Enhance the prediction efficiency by incorporating solutions from surrogate models.
Expected Profile
Essential Qualifications
15. Master’s degree (or equivalent) in Mechanical/Civil/Computational Engineering, or related.
16. Strong background in numerical methods in engineering, computational mechanics, modelling and simulation in CFD/FEA.
17. Experience with scientific programming (at least Python and C++).
18. Excellent written and spoken English.
19. Very strong team working skills in international, interdisciplinary settings.
20. Very good self organization.
Desirable Skills
21. Very good knowledge of fluid–structure interaction (FSI).
22. Good experience with digital twins, model updating, or structural dynamics.
23. Understanding of optimization, inverse problems, or sensitivity analysis.
24. Familiarity with surrogate models (ROMs, ML-based surrogates).
25. Motivation for renewable energy and wind turbine technology.
What We Offer
26. Fully funded MSCA Doctoral Network position.
27. Participation in a cutting‑edge research project with high societal importance
28. Vibrant and inspiring research environment within an international multidisciplinary team.
29. Working at one of the leading technical universities in Europe.
30. Competitive salary and mobility allowance per The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance.
How to Apply
Please submit the following to with the subject "Application for COMBINE DC position"
Curriculum Vitae. Motivation letter describing your research interests and specific fit to the offered position. Relevant certificates and diplomas, transcript of records. Contact details of at least two references.
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