Research Associate / PhD Student (f/m/d) - Development of methodologies for high fidelity digital twins targeting industry scale wind turbines
Research Associate / PhD Student (f/m/d) - Development of methodologies for high fidelity digital twins targeting industry scale wind turbines
Research Associate / PhD Student (f/m/d) - Development of methodologies for high fidelity digital twins targeting industry scale wind turbines
Research Associate / PhD Student (f/m/d) - Development of methodologies for high fidelity digital twins targeting industry scale wind turbines
Technische Universität München
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Research Associate / PhD Student (f/m/d) - Development of methodologies for high fidelity digital twins targeting industry scale wind turbines
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Research Associate / PhD Student (f/m/d) - Development of methodologies for high fidelity digital twins targeting industry scale wind turbines
23.01.2026, Wissenschaftliches Personal
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:
- Application deadline: 30.04.2026
- Starting date: 01.09.2026
- Position type: full time
- Position duration: 3 years
Research Objectives
- Development of sensitivity framework for coupled sensitivity analysis.
- Extend the developed framework to support FSI problems, and identify suitable sensitivity computation methods.
- Identify important modelling parameters for the digital model.
- Create a digital model of the wind turbine whilst having the important modelling parameters variable.
- Develop methodologies to solve coupled inverse problems.
- Use the measurement / test data to identify the high-fidelity modelling parameters by solving the inverse problem.
- Validate the digital model against test scenarios.
- Perform what-if analyses for the developed digital models.
- Develop interfaces to provide feedback from the digital twin to the physical turbine.
- Enhance the prediction efficiency by incorporating solutions from surrogate models.
Expected Profile
Essential Qualifications
- Master’s degree (or equivalent) in Mechanical/Civil/Computational Engineering, or related.
- Strong background in numerical methods in engineering, computational mechanics, modelling and simulation in CFD/FEA.
- Experience with scientific programming (at least Python and C++).
- Excellent written and spoken English.
- Very strong team working skills in international, interdisciplinary settings.
- Very good self organization.
Desirable Skills
- Very good knowledge of fluid–structure interaction (FSI).
- Good experience with digital twins, model updating, or structural dynamics.
- Understanding of optimization, inverse problems, or sensitivity analysis.
- Familiarity with surrogate models (ROMs, ML-based surrogates).
- Motivation for renewable energy and wind turbine technology.
What We Offer
- Fully funded MSCA Doctoral Network position.
- Participation in a cutting‑edge research project with high societal importance
- Vibrant and inspiring research environment within an international multidisciplinary team.
- Working at one of the leading technical universities in Europe.
- Competitive salary and mobility allowance per MSCA rules .
- Joint academic–industrial supervision and mentoring opportunities.
- Access to state-of-the-art data and industrial test cases.
- Structured research and transferable-skills training.
- International secondments with leading European partners.
- Very good preparation for career pathways in academia and industry.
Eligibility (MSCA Rules)
- Eligible for pursuing a PhD at TUM.
- Early-stage researcher (no PhD awarded at the time of recruitment).
- The EU Mobility Rule applies. That is, the candidate must not have resided or carried out her/his main activity (work, studies, etc.) in Germany for more than 12 months in the 3 years immediately before the recruitment date. Compulsory national service, short stays such as holidays, and time spent as part of a procedure for obtaining refugee status under the Geneva Convention are not taken into account.
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 bewerbung.st@ed.tum.de 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.
All the other positions offered by COMBINE can be found in https://euraxess.ec.europa.eu/jobs/401249
Die Stelle ist für die Besetzung mit schwerbehinderten Menschen geeignet. Schwerbehinderte Bewerberinnen und Bewerber werden bei ansonsten im wesentlichen gleicher Eignung, Befähigung und fachlicher Leistung bevorzugt eingestellt.
Kontakt: suneth.warnakulasuriya@tum.de
