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Postdoctoral Researcher on Short-Term Wind and Solar Power Forecasting

Key information

  • Publication date:22 April 2025
  • Workload:100%
  • Place of work:Lausanne
  • Salary estimate from jobup.ch:Log in, to see estimate from jobup.ch

Job summary

Join EPFL as a Postdoctoral Researcher in renewable energy forecasting. Collaborate in a dynamic environment with excellent benefits.

Tasks

  • Develop hybrid forecasting models for wind and solar energy.
  • Analyze large datasets and employ AI techniques for predictions.
  • Contribute to the UrbanTwin project focusing on climate action.

Skills

  • PhD in relevant field with machine learning experience required.
  • Proficiency in Python and data analysis techniques.
  • Strong English communication and publication record essential.

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Postdoctoral Researcher on Short-Term Wind and Solar Power Forecasting

Swiss Federal Institute of Technology Lausanne, EPFL
Workplace Lausanne, Lake Geneva region, Switzerland Category
Environment | Computer Science
Position
Senior Scientist / Postdoc
Published 19 April 2025
EPFL, the Swiss Federal Institute of Technology in Lausanne, is one of the most dynamic university campuses in Europe and ranks among the top 20 universities worldwide. The EPFL employs more than 6,500 people supporting the three main missions of the institutions: education, research and innovation. The EPFL campus offers an exceptional working environment at the heart of a community of more than 18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries.

Postdoctoral Researcher on Short-Term Wind and Solar Power Forecasting

Main duties and responsibilities

The Wind Engineering and Renewable Energy (WiRE) Laboratory at the École polytechnique fédérale de Lausanne (EPFL) is looking to fill a post-doctoral position in the field of short-term (typically from 6h to 24h) power forecasting of renewable energy sources (wind and solar). The successful post-doc candidate will work in the development of hybrid forecasting models combining numerical weather prediction (NWP) models with artificial intelligence (AI) techniques for wind and solar energy production.

Mission

The research framework will involve data collected from various wind and solar power plants in Switzerland and worldwide. By using long time series of wind/solar power production, as well as NWP outputs (big data), the candidate will focus on the development and testing of new forecasting models, with especial emphasis in novel AI-based methods (e.g., deep learning, explainable and physics-informed AI, large language models, etc.).

The position will be developed within the funded project entitled "UrbanTwin: An urban digital twin for climate action: Assessing policies and solutions for energy, water and infrastructure", as part of the ETH-Joint Initiative funding program.

Profile

In order to qualify for the position, the candidates are required to have:

  • A completed PhD degree.
  • Experience in machine-learning methods.
  • Some skill in at least one of these topics :


- Large data sets analysis

- Statistics and uncertainty analysis (probabilistic)

- Weather modelling (Numerical Weather Prediction)

  • Computing experience with Python.
  • Excellent English writing and speaking skills.
  • Strong peer-reviewed journal publication record.


We offer

  • A stimulating and international working environment in the EPFL campus/Lausanne.
  • Competitive salary and excellent working conditions.
  • Opportunity to perform state-of-the-art research in one of the most dynamic scientific institutions in Europe.


Informations

Contract Start Date : as soon as possible

Activity Rate : 100%

Contract Type: CDD

Duration: one year with possibility of renewal

Reference: Please send a single PDF file including CV, a brief research statement and the contact details of 3 reference persons



For any further information, please contact Prof. Porté-Agel at fernando.porte- E-Mail schreiben














In your application, please refer to myScience.ch and referenceJobID67082.

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