Programming, Computer engineering
Work description
The goal of this research is to evaluate how Large Language Models (LLMs) can be leveraged to address forecasting challenges in the energy sector, specifically those that can be framed as sequence-to-sequence problems. The grant holder will explore different strategies on prompt engineering, compare the potential of different LLM’s and create a methodology that can be replicable to multiple challenges for the energy sector.
Academic Qualifications
Electrical Engineering, Informatics, Computer Science or similar courses
Minimum profile required
Programming experience in PythonBasic understanding of machine learning conceptsExperience using data science tools (e.g., pandas, scikit-learn, matplotlib)Familiarity with version control systems (Git)
Preference factors
Familiarity with Large Language Models (e.g., Mistral, LLaMA) Understanding of machine learning workflows and forecasting methods Python oriented to data manipulation and analytics
Application Period
Since 01 Jun 2025 to 30 Jun 2025
Centre
Power and Energy Systems