Electric power systems
[Closed]
Work description
The technological revolution in the energy sector is producing large volumes of data with valuable impact in the business and functional processes of system operators, generation companies and grid users. The research fellow will develop innovative data-driven techniques (e.g., machine learning) for optimization (e.g., energy efficiency) and forecasting problems. It will join a multidisciplinary team referenced internationally for its high expertise in energy analytics, within the context of smart grids.
Academic Qualifications
Master degree in Electrical Engineering or Mathematics or similar
Minimum profile required
Advance knowledge in machine learningAdvance knowledge in programming (e.g., Python)Fluent in English (spoken and written)
Preference factors
Experience with energy systems related problems Experience with time series forecasting problems
Application Period
Since 18 Nov 2019 to 29 Nov 2019
[Closed]
Cluster / Centre
Power and Energy / Power and Energy Systems