PhD Position/Research Assistant - Analysis for lidar-based minute-scale power forecasting of Offshor

PhD Position/Research Assistant - Analysis for lidar-based minute‑scale power forecasting of Offshore Wind Farms

About Us

Wind energy research at the Carl von Ossietzky Universität Oldenburg has gained international recognition by its integration into ForWind – Center for Wind Energy Research of the Universities of Oldenburg, Hannover and Bremen and the national Wind Energy Research Alliance of the German Aerospace Center (DLR), Fraunhofer Institute for Wind Energy Systems (IWES) and ForWind. At ForWind, we value and maintain collaboration between our research groups and partner institutions, including members of the European Academy of Wind Energy. In Oldenburg, our 50 researchers from physics, meteorology, and engineering collaborate at the “Research Laboratory for Turbulence and Wind Energy Systems”, which is centred on wind physics. Our mission is to develop a deeper understanding of the atmospheric and wind power plant flow physics required to meet the global demand for clean, affordable electricity. Therefore, we conduct laboratory experiments, free‑field measurements and HPC‑based numerical simulations. The main topics include the description and modelling of wind turbulence, the analysis of interactions of turbulent atmospheric wind flow and wind energy systems, as well as the control of wind turbines and wind farms. The covered scales range from small‑scale turbulence up to meteorological phenomena. Our research facilities comprise three turbulent wind tunnels, various equipment for free‑field measurements at on‑ and offshore wind farms and a high‑performance computing cluster. Almost all our projects combine analyses at more than one of these infrastructures. For instance, virtual lidar measurements can be performed in simulated three‑dimensional flow fields to verify the analysis algorithms. Our multi‑lidar systems, equipped with up to three scanning lidars, are particularly important for the abovementioned research.

Further information is available at and

Your Tasks

The increasing share of renewable energy in today's energy system drives the need for continuous power forecasts at the minute scale. Such forecasts are important for ensuring grid stability, reducing costs associated with feed‑in management, and supporting electricity trading. We use scanning Doppler wind lidars to characterise the inflow several kilometres ahead of offshore wind farms, enabling reliable forecasting of wind turbine power for up to 30 minutes. The accurate prediction of so‑called 'wind ramps', i.e. strong and sudden changes in wind speed or direction, is particularly crucial. To make lidar‑based forecasts more practical for industrial applications, they need further development, particularly concerning the forecast horizon, measurement setup, measurement trajectories, and the prediction of wind farm effects.

• processing large amounts of data by combining lidar measurements, meteorological information, and operational data from wind farms;
• further developing forecasting methods and implementing and validating the developed forecasting algorithms (physics‑based and physics‑informed machine learning) for real‑time applications;
• analysing the uncertainty of input data and forecasts and developing methods to mitigate or account for these uncertainties;
• supporting the operation of extensive offshore measurement campaigns;
• presenting scientific results at international conferences and through peer‑reviewed publications to extend your specific network;
• cooperating closely with the researchers at ForWind and the other industrial and scientific partners in different research projects.

Your Profile

• qualifying university master's degree in Physical Science, Mechanical or Aerospace Engineering, Renewable Energy or equivalent;
• profound knowledge and relevant experience in handling and analysing large data sets and statistical analysis;
• comprehensive skills with measurement techniques and uncertainty estimation;
• knowledge in forecasting methods and machine learning;
• extensive experience in programming with Python;
• high motivation and the ability to work jointly on a complex research topic;
• fluency in communicating and reporting in English.

We Offer

We offer you the opportunity to develop your scientific career in a young and lively academic environment. You will be working in the WindLab – one of the university's most modern office and lab spaces – while you will also have the opportunity to do flexible and mobile work. Your pathway towards the PhD is actively supported by, e.g.,

• multidisciplinary cooperation with other researchers at ForWind and Fraunhofer IWES in Oldenburg;
• direct collaboration with industry while maintaining the links with our national and international partners in academia, including a PhD network;
• optional secondment at an international research institute;
• development of personal, scientific, and teaching skills through an individual training programme and selected teaching tasks;
• opportunities to present scientific results at international conferences and through peer‑reviewed publications to extend your specific network;
• structured supervision of the PhD process.

Further, the university fosters a family‑friendly working environment and offers a family service centre and on‑campus children's daycare.

Our Standards

The University of Oldenburg is dedicated to increase the percentage of female employees in the field of science. Therefore, female candidates are strongly encouraged to apply. In accordance to

• 21 Section 3 NHG, female candidates with equal qualifications will be preferentially considered. Applicants with disabilities will be given preference in case of equal qualification.

Further Information

The employment is initially limited to three years. The payment is based on the collective agreement for the public service in the German federal states, TV‑L E13, for a 75% position.