Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Delft University of Technology (TU Delft)
- Eindhoven University of Technology (TU/e)
- European Space Agency
- University of Twente
- University of Twente (UT)
- Erasmus University Rotterdam
- Erasmus University Rotterdam (EUR)
- KNAW
- Radboud University
- Tilburg University
- University of Amsterdam (UvA)
- Wageningen University & Research
- 2 more »
- « less
-
Field
-
Researcher in AI-based Load Forecasting at Radboud University, you will be at the heart of this challenge. You will develop cutting-edge AI models that predict electricity consumption at both grid and
-
to react to the wind before it hits the blades. Using upstream LiDAR measurements (taken several rotor diameters ahead), you will develop a wind field forecasting method, leveraging principles like Taylor’s
-
Defence Academy invites applications for a fully-funded postdoctoral position in the interdisciplinary area of AI-driven scenario forecasting. This position is in collaboration with the Data Science Center
-
Vacancies EngD Position: LiDAR-Assisted Wind Field Forecasting for Next-Gen Turbines Key takeaways About the Project Wind energy is a cornerstone of the global energy transition, but increasing
-
) conditions. Your work will provide the "ground truth" for the project. By simulating complex inflow conditions, you will create the high-fidelity datasets required to validate the Wind Field Forecasting (WFF
-
work will provide the "ground truth" for the project. By simulating complex inflow conditions, you will create the high-fidelity datasets required to validate the Wind Field Forecasting (WFF) models
-
To Impacts For Improved Attribution, Forecasting And Regional Responses) brings together 19 academic and non-academic partners from three continents with the scope of advancing the knowledge and practices
-
Introduction About SUNRISE SUNRISE (Extreme Events In A Warming, Unequal World: Linking Drivers To Impacts For Improved Attribution, Forecasting And Regional Responses) brings together 19 academic and non
-
external partners who drive innovation and growth, and our research is strongly embedded in applications such as anomaly detection, process monitoring and improvement, weather and climate forecasting, and
-
on correlation-based machine learning. When an agricultural system fails due to compounding climate extremes - like a simultaneous heatwave, drought, and ozone pollution spike - standard models can forecast the