This fully funded PhD scholarship at the Technical University of Denmark (DTU) offers an exceptional opportunity to advance research in uncertainty quantification and technology qualification for next-generation wind turbine components. Hosted by DTU Wind and Energy Systems and embedded within the prestigious MET2ADAPT Marie Skłodowska-Curie Doctoral Network, the project focuses on developing robust frameworks for validating the safety, performance, and longevity of wind turbine blades enhanced with adaptive meta-materials.

As a doctoral candidate, you will employ advanced probabilistic and statistical modelling techniques—including Bayesian inference, stochastic simulation, and machine learning—to address uncertainties in mechanical and aerodynamic properties, manufacturing tolerances, and environmental factors affecting turbine blades. Your work will directly contribute to the creation of a technology qualification framework that supports compliance with international certification standards (such as IEC and DNV), streamlining the commercialization of innovative turbine designs.

The research is highly interdisciplinary, combining mechanical engineering, mathematics, computer science, and materials science. You will collaborate closely with leading industrial and academic partners, including NADARA, and benefit from secondments at Columbia University (USA), University of Trento (Italy), and University of Granada (Spain). These placements will provide advanced training in stochastic modelling, meta-materials, and reliability analysis, further enhancing your expertise and professional network.

DTU’s Risø campus, a global hub for wind energy research, offers a collaborative and industry-connected environment. The MET2ADAPT network provides thematic training weeks, specialist workshops, and transferable-skills courses, ensuring comprehensive interdisciplinary development. You will join a cohort of 16 international PhD researchers, gaining exposure to both cutting-edge research and real-world operational challenges in renewable energy.

Applicants must hold a two-year master’s degree (or equivalent) in engineering, applied mathematics, physics, data science, or a related field, with strong skills in computational modelling, statistics, or machine learning. Experience or interest in uncertainty quantification, probabilistic modelling, and wind turbine component validation is essential. Additional expertise in scientific programming (Python or MATLAB), structural mechanics, material modelling, multi-physics simulation, digital-twin technologies, or reliability assessment is highly advantageous. Candidates must not already hold a doctoral degree and must comply with the MSCA mobility rule (not have resided or carried out main activity in Denmark for more than 12 months in the 3 years prior to recruitment).

The position is fully funded for 3 years under the MSCA DN programme, with salary and terms based on the Danish Confederation of Professional Associations’ agreement. The application deadline is 10 February 2026. For further information, contact Professor Athanasios Kolios ([email protected]) or visit the DTU Wind website. To apply, submit your application and supporting documents as a single PDF via the online portal before the deadline.