Summary: PhD opportunity focused on developing next-generation AI-based forecasting models to improve renewable energy integration, forecast accuracy, and real-time decision-making, with emphasis on spatial-temporal modeling, uncertainty quantification, and edge deployment.
PhD Position in Advanced AI-Based Forecasting Models
Designation: PhD Position, Tallinn University of Technology (TalTech), Estonia
Table:
- Research Area: Advanced AI-based forecasting for renewable energy; spatial-temporal modeling; weather-integrated forecasting; edge deployment; uncertainty quantification
- Location: Tallinn, Estonia (TalTech)
- Eligibility/Qualification: Master’s degree in electrical engineering, computer science, or related field; strong ML/AI background; time-series and energy systems knowledge; programming skills (Python, MATLAB, R); English at CEFR C1 or higher
- Responsibilities/Tasks: Develop AI models (GNNs, CNN-LSTM, Transformers); integrate multi-source weather and energy data; design hybrid AI architectures; implement probabilistic and explainable AI; optimize for real-time edge deployment
- Supervisors: Main supervisor — Adjunct Professor Avleen Kaur Malhi; Co-Supervisor — Researcher Noman Shabbir (TalTech)
- Institution: Tallinn University of Technology (TalTech) – Department of Electrical Power Engineering and Mechatronics
- Application Window: 18.06.2026 to 18.07.2026
- Contact: avleen.malhi@taltech.ee; noman.shabbir@taltech.ee; docstudy@taltech.ee
- How to Apply: Submit a motivational/essay on the most intriguing research questions and future directions, including potential expansions of listed research questions
Research Area:
- Focus on designing, developing, and deploying an integrated suite of AI models for renewable energy forecasting
- Address spatial and temporal variability, weather data integration, uncertainty quantification, interpretability, and edge deployment
- Explore deep learning, probabilistic modeling, and edge computing to improve forecasting accuracy and usability in real-world energy systems
Location:
- Tallinn University of Technology (TalTech), Estonia
- Department of Electrical Power Engineering and Mechatronics
Eligibility/Qualification:
- Master’s degree in Electrical Engineering, Computer Science, or a related field
- Strong background in machine learning / AI and data analytics
- Understanding of time-series analysis and energy systems
- Proficient programming and data analytics skills (Python, MATLAB, R)
- Proficient English (CEFR C1 or higher)
- Excellent problem-solving and analytical skills; ability to work independently and in an international team
- Willingness to contribute to organizational tasks
Job Description / Responsibilities:
- Develop advanced AI models (GNNs, CNN-LSTM, Transformers) to capture spatial-temporal patterns in weather and energy data
- Integrate multi-source data (numerical weather predictions, real-time inputs) for short- and long-term forecasting
- Design hybrid AI architectures; apply attention mechanisms and evolutionary algorithms
- Implement probabilistic and explainable AI methods to quantify uncertainty and ensure interpretable predictions
- Optimize models for real-time deployment on edge devices with efficient architectures and federated learning approaches
How to Apply:
- Submit a motivational essay detailing the most intriguing research questions and future elaboration aspects relevant to AI-based energy forecasting
- Consider proposing expansions to the listed research questions
Last Date for Apply: 18 July 2026
