PhD in Wind Wake Dynamics & Data-Driven Modelling

Danmarks Tekniske Universitet; Ørsted; On-site; DKK 335,000 - 420,000; Full time

Benefits offered by this job

PhD stipend

International collaboration

Institute funding

Job summary

DTU Wind and Energy Systems in Denmark seeks a motivated PhD candidate to explore how nonlinear interactions in turbulent flows affect wind turbine wakes. The project combines fluid mechanics, high-fidelity simulations, and data-driven reduced-order modelling to advance wake dynamics under realistic atmospheric conditions.

The PhD is conducted with Imperial College London and Professor Oliver Buxton, including an extended stay at Imperial to bridge simulations and experiments and develop novel

Qualifications

• Background in engineering, physics or similar.
• Understanding of fluid mechanics, turbulence, boundary layers, wake aerodynamics.
• Understanding of dynamical systems.
• Experience developing data-driven methods (POD, autoencoders) and spectral analysis.
• Ability to work with large data sets.
• Experience with scientific programming, e.g. Python.
• Clear and concise communication skills in English.
• Positive attitude, strong drive, critical thinking, and eagerness to learn.
• Two-year master’s degree (120 ECTS) or equivalent.

Responsibilities

• Perform CFD simulations of wind turbine wake aerodynamics across various conditions.
• Investigate data-driven methods for accuracy and efficiency to develop wake models.
• Conduct uncertainty quantification and validate data-driven models against high-fidelity results.
• Provide physical interpretation of constructed models.
• Participate in conferences and publish results in journals.

Skills

Turbulence theory

Wake modelling

Data-driven models

POD

Autoencoders

Dynamical systems

Large data handling

Python

English communication

Education

Master's degree (2-year)

Tools

High-performance computing

CFD

Job description

DTU Wind and Energy Systems in Denmark seeks a motivated PhD candidate to explore how nonlinear interactions in turbulent flows affect wind turbine wakes. The project combines fluid mechanics, high-fidelity simulations, and data-driven reduced-order modelling to advance wake dynamics under realistic atmospheric conditions.

The PhD is conducted with Imperial College London and Professor Oliver Buxton, including an extended stay at Imperial to bridge simulations and experiments and develop novel