Enhancing Renewable Energy Integration Through Advanced AI-Based Forecasting Models
We invite applications for a PhD position focused on developing next-generation AI-based forecasting models to enhance renewable energy (RES) integration into power systems. This research emphasizes improving prediction accuracy for renewable energy production (particularly solar and wind) through advanced machine learning techniques that capture spatial-temporal dependencies, quantify uncertainty, and enable real-time decision-making. The project will explore hybrid AI architectures, weather-integrated forecasting systems, and edge-deployable intelligent models. The goal is to improve renewable energy planning, grid stability, and operational efficiency through reliable and interpretable forecasting tools. Applicants should have a master’s degree in electrical engineering, computer science, or a related field, strong analytical and programming skills, and an interest in AI-driven energy systems. The PhD candidate is expected to publish in high-impact journals, present at international conferences, and contribute to advancing intelligent renewable energy forecasting systems.
About the project:
This PhD project focuses on designing, developing, and deploying a holistic suite of advanced AI models for renewable energy forecasting. The research will address key challenges in modeling spatial and temporal variability, integrating weather prediction systems, handling uncertainty, and ensuring interpretability and real-time deployment. The successful candidate will work on cutting-edge AI techniques, combining deep learning, probabilistic modeling, and edge computing to improve forecasting accuracy and usability in real-world energy systems.
Key research questions:
- How can spatial and temporal dependencies in renewable energy systems be effectively modeled?
- How can weather forecasting systems be integrated with AI models for improved prediction accuracy?
- How can uncertainty in renewable energy forecasting be quantified and utilized for better decision-making?
- How can AI models be optimized for real-time processing on resource-constrained devices?
- How can interpretability be ensured for complex AI models used in energy systems?
Responsibilities and (foreseen) tasks:
- Develop advanced AI models (GNNs, CNN-LSTM, Transformers) to capture spatial-temporal patterns in weather and energy data.
- Integrate multi-source data, including numerical weather predictions and real-time inputs, for short- and long-term forecasting.
- Design hybrid AI architectures and optimize them using advanced techniques such as attention mechanisms and evolutionary algorithms.
- Implement probabilistic and explainable AI methods to quantify uncertainty and ensure transparent, interpretable predictions.
- Optimize models for real-time deployment on edge devices using efficient architectures and federated learning approaches.
Applicants should fulfil the following requirements:
- Master’s degree in electrical engineering or computer science or related field
- Strong background in machine learning / AI and data analytics
- Understanding of time-series analysis and/or energy systems
- Proficient programming & data analytics skills (e.g., Python, MATLAB, R)
- Proficient English language user (at least CEFR level of C1)
- Excellent problem solving and analytical skills
- Capacity to work both as an independent researcher and part of an international team
- Willingness to aid in relevant organizational tasks
The candidate should submit a motivational essay on the topic, including the most intriguing research and future elaboration aspects for the candidate, in relevant technical matters.The candidate can propose on the expansion of listed research questions that candidate would see most motivating challenges to solve.
The following experience is beneficial:
- Experience with deep learning frameworks (e.g., PyTorch, TensorFlow)
- Published scientific papers
- Practical experience in working with MATLAB/Python/R
- Knowledge of Graph Neural Networks, Transformers, or probabilistic modeling
- Experience with distributed or edge AI systems
We offer:
- 4-year PhD position in one of the largest, most internationalized and leading social science research centers in Estonia with a large portfolio of ongoing pan-European and national public administration, digital governance and innovation studies projects
- The chance to do high-level research in one of the most dynamic digital government contexts globally
- Opportunities for conference visits, research stays and networking with globally leading universities and research centers in the fields of public administration, innovation studies and digital government
Supervisors:
Main supervisor: Adjunct Professor Avleen Kaur Malhi: Office of Vice-Rector for Research: FinEst Centre for Smart Cities
Co-Supervisor: Researcher Noman Shabbir: Office of Vice-Rector for Research: FinEst Centre for Smart Cities
Tallinn University of Technology (TalTech) is an international scientific community with approximately 9,000 students and 2,000 employees; it is one of the largest universities in Estonia, the leading EU country in digitalisation. The university's strengths are broad multidisciplinary study/research interests, a modern research environment, and strong collaboration with international educational and research institutions. TalTech is aiming to be an organisation leading the way to a sustainable digital future.
The Department of Electrical Power Engineering and Mechatronics of Tallinn University of Technology is an interdisciplinary research center that focuses on socially relevant and future-oriented research and teaching issues related to power engineering and mechatronics. The mission of the Department is to be a leader in electrical engineering and technical studies and development projects in Estonia, known and valued in society, and a respected partner in both national and international cooperation networks and organizations. The department has coordinated and partnered with several international projects, including Horizon 2020, INTERREG, 7FP, Nordic Energy Research etc. The Department of Electrical Power Engineering and Mechatronics conducts research within 7 research groups and operates state-of-the-art laboratories with high-end equipment, offering also accredited services in the fields of lighting and different electrical measurements. The focus areas of the department are related to domestic and global challenges related to increasing digitalization, decarbonization and decentralization of electric power systems and increasing us of renewable energy sources.
For information about the admission process, please visit the PhD Admission homepage.
Applications can be submitted from 018.06.2026 to 18.07.2026
