PhD Scholarship in Fast and Accurate Machine-Learning Surrogates of Atmospheric Flow Dynamics
1 month ago
This PhD project aims to explore advanced data-driven methods to accelerate the green transition by providing fast and accurate modeling of wind farm wake aerodynamics. The project will investigate modern machine-learning methods for dimension reduction, synthetic turbulence generation, and regression across an expanded parameter range of application.
Responsibilities and Qualifications- Develop efficient dimension reduction methods to approximate wind farm flows across various operational and atmospheric conditions.
- Generate synthetic turbulence based on stochastic model or deep-learning techniques.
- Construct regression models capturing changes in turbulent structures for the various conditions.
- Compare different machine-learning techniques in terms of accuracy and computational efficiency.
- Perform detailed validation and error estimation of the models.
- Provide physical interpretation of the constructed models.
- Participate in scientific conferences and publish results in scientific journals.
- A background in data science, physics, engineering, or similar.
- Experience developing and using machine-learning techniques, e.g. neural networks.
- Ability to work with large data sets.
- Scientific programming experience, e.g. Python.
- Understanding of fluid mechanics, turbulence, boundary-layer flows and/or time series analysis is beneficial.
- Clear and concise communication skills in English.
- Positive attitude, a strong drive, critical thinking, and an eagerness to learn.
The assessment of the applicants will be made by Associate Professor Søren Juhl Andersen, Research Juan Pablo Murcia Leon, Professor Jens Nørkær Søresen from DTU as well as Associate Professor Ricardo Vinuesa from KTH.
The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The period of employment is 3 years.
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