Unlock the Power of Machine Learning for Sustainable Energy Solutions

4 days ago


Kongens Lyngby, Lyngby-Tårbæk Kommune, Denmark Danmarks Tekniske Universitet (DTU) Full time
Job Description

At Danmarks Tekniske Universitet (DTU), we are committed to driving innovation and excellence in research, education, and scientific advice. We seek a highly motivated PhD candidate to join our team in exploring advanced data-driven methods to accelerate the green transition by providing fast and accurate modeling of wind farm wake aerodynamics.

This PhD project will focus on expanding an existing framework for fast and accurate modeling of wind farm wake aerodynamics. The successful candidate will develop efficient dimension reduction methods to approximate wind farm flows across various operational and atmospheric conditions, generate synthetic turbulence based on stochastic models or deep-learning techniques, and construct regression models capturing changes in turbulent structures for the various conditions.

The candidate is expected to have a strong background in data science, physics, engineering, or a related field, with experience in developing and using machine-learning techniques, such as neural networks. They should also be able to work with large data sets and have scientific programming experience, preferably in Python.

We offer a competitive salary and benefits package, including access to cutting-edge research facilities and collaboration opportunities with international partners. As a PhD candidate at DTU, you will have the opportunity to contribute to ground-breaking research and shape your career in a dynamic and supportive environment.

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 models 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.
Benefits
  • A competitive salary of approximately 38,000 - 45,000 DKK per month (gross).
  • A comprehensive benefits package, including access to cutting-edge research facilities and collaboration opportunities with international partners.
  • Opportunities for professional development and growth within a dynamic and supportive environment.


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