PhD in Advanced AI-Based Forecasting Models for Renewable Energy Integration Tallinn University of Technology in Estonia

PhD position at Tallinn University of Technology (TalTech) in advanced AI-based forecasting models for renewable energy integration.

The project focuses on developing next-generation forecasting methods for renewable energy production, especially solar and wind, to improve power-system planning, grid stability, and operational efficiency. Research topics include machine learning, deep learning, time-series analysis, probabilistic modeling, uncertainty quantification, weather-integrated forecasting, and edge-deployable AI. The work mentions architectures such as GNNs, CNN-LSTM, and Transformers, plus explainable and real-time models for energy systems.

Applicants should have a Master’s degree in electrical engineering, computer science, or a related field, along with strong programming and analytics skills. A good command of English is required (at least CEFR C1). Experience with PyTorch, TensorFlow, MATLAB, Python, R, Graph Neural Networks, probabilistic modeling, or edge/distributed AI is listed as beneficial.

The position is a 4-year PhD opportunity. The post highlights opportunities for conference visits, research stays, and networking, but does not specify a stipend or tuition details. The application window is 18 June 2026 to 18 July 2026, and the deadline is 2026-07-18.

Supervisors listed are Dr. Avleen Malhi (main supervisor) and Noman Shabbir (co-supervisor), both affiliated with TalTech / the FinEst Centre for Smart Cities and the Office of the Vice-Rector for Research.