PhD Position in Data-Informed Reduced Order Modelling Wind Energy Structures

PhD Position in Data‑Informed Reduced Order Modelling for Wind Energy Structures

The Chair of Structural Mechanics and Monitoring at ETH Zürich seeks to appoint a highly motivated PhD candidate for a fully funded, international doctoral position within the Marie Skłodowska-Curie Doctoral Networks (MSCA‑DN) project Coupled Problems for Decarbonization in Industry and Power Generation (COMBINE). COMBINE brings together a consortium of leading academic and industrial partners to tackle advanced modelling, simulation, sensing, and data analysis challenges in engineering systems across sectors.

The COMBINE Doctoral Network aims to train a cohort of 17 doctoral candidates to address research challenges in coupled problems relevant to decarbonization, advanced simulation, sensing, and data‑driven methods for energy and industrial systems. Through cross‑institutional collaboration and industry engagement, COMBINE provides structured doctoral training, secondments, and interdisciplinary research experiences.

PhD position COMBINE‑DC17 is hosted and matriculated at ETH Zurich, Switzerland, focusing on data‑informed reduced order modelling for coupled problems in wind energy structures. This research contributes to the development of advanced adaptive reduced order model (ROM) frameworks tailored for fluid–structure interaction (FSI) and real‑time monitoring/control challenges in wind turbine systems.

As a doctoral candidate in this role, you will be part of an international research environment, benefit ting from network‑wide training activities, interdisciplinary collaborations, and planned secondments to other COMBINE partner institutions. You will work at the interface of numerical simulation, data‑driven modelling, and engineering applications for sustainable wind energy systems.

The doctoral research will involve:

• Developing adaptive reduced order model frameworks that integrate real‑time monitoring data and virtual sensing to select and adjust model fidelity dynamically.
• Designing and validating virtual sensing methods to inform model adaptation and performance evaluation.
• Implementing ROM methodologies for fluid–structure interaction in wind turbine systems, balancing accuracy and computational efficiency.
• Validating adaptive ROM performance with high‑fidelity model data and experimental or sensor‑enhanced monitoring outputs.
• Demonstrating the capability of adaptive ROMs for real‑time simulation, decision support, and control applications in offshore wind energy contexts.

This position is offered for 36 months, with the earliest starting date as agreed upon recruitment. The primary academic advisor is Prof. Eleni Chatzi (ETH Zurich, D‑BAUG), with additional mentorship provided during secondments at planned collaborating institutions (UGENT, TUM, and CIMNE).

Applicants must hold a:

• M.Sc. Diploma (120 ECTS points) or equivalent in civil, mechanical or electrical engineering, geosciences, physics, applied mathematics, computer sciences or related fields, and be at the beginning of their research career.
• Principal qualifications include strong analytical and quantitative skills in numerical analysis, programming, high‑performance computing, as well as skills in dynamics & structural health monitoring, data analysis and modelling, and interest in laboratory‑based experimentation and engineering applications.
• A solid knowledge of English as the spoken and written language of work is mandatory.

We expect good interpersonal skills, the ability to thrive in a diverse, multidisciplinary environment, ability to present work in international conference, as well as the willingness to spend a number of months working with project collaborators at partner universities in this project.

Eligibility Criteria:
Researchers can be of any nationality. At the time of recruitment, researchers:

• Should not have been awarded a title of PhD (Applicants who have successfully defended their doctoral thesis but not yet formally been awarded the doctoral degree will not be considered eligible).
• Should not have resided or carried out your main activity in Switzerland, for more than 12 months within the last 3 years.