Hybrid Modelling of Thermal Grids (Physics-Informed Data-Driven Approach)
We are seeking a highly motivated PhD candidate to join our research team in developing hybrid modelling approaches for heating systems. The research will combine physics-based modelling with data-driven methods to improve the performance, reliability, and efficiency of thermal grids.
District heating and cooling systems play an important role in the transition toward sustainable and low-carbon energy systems. Their operation requires accurate and computationally efficient models capable of capturing complex thermal dynamics. Traditional physics-based models provide strong interpretability but can be computationally demanding, while purely data-driven approaches may lack robustness and physical consistency. Recent developments in physics-informed machine learning (and digital twin) techteamdasnologies enable the integration of physics-based modelling with operational data, supporting improved prediction, model calibration, and optimisation of thermal grids.
Research Objective:
The objective of this PhD project is to develop hybrid modelling approaches for heating and cooling energy systems by integrating physics-based models with data-driven techniques. The research will focus on the development of control-oriented models capable of representing the dynamic behaviour of energy systems. The research will investigate physics-informed modelling methods that combine simulation models with operational data to improve model calibration, predictive performance and computational efficiency. The developed models will support optimisation and advanced control strategies and contribute to the development of digital twin frameworks for thermal energy infrastructures.
Key Challenges:
1. Data Integration and Model Calibration: Integrating physics-based models with operational datasets while ensuring reliable parameter estimation and model calibration.
2. Hybrid Model Development: Developing scalable modelling approaches that combine physical models with machine learning while maintaining interpretability and computational efficiency.
3. Model Validation and Practical Relevance: Validating the developed models through case studies and ensuring applicability for real-world district heating systems while accounting for model uncertainties.
Desired Candidate Profile:
- A strong background in applied mathematics, control theory, or machine learning.
- Experience in data-driven modelling, particularly with time-series data.
- Familiarity with dynamical systems theory and numerical optimization.
- Programming skills (e.g., Python, MATLAB) for handling large datasets.
Impact and Expected Outcomes:
The successful candidate will contribute to the development of advanced hybrid modelling methodologies for heating systems. The outcomes of this project will enable the creation of physics-informed digital twins and control-oriented models that support improved monitoring, optimisation, and operation of district heating infrastructures. These results will contribute to the digitalisation and decarbonisation of thermal energy systems and support more efficient operation of thermal grids.
Supervisors:
Main supervisor: Associate Professor Kristina Vassiljeva: School of Information Technologies: Department of Computer Systems: Centre for Intelligent Systems
Co-Supervisor: Tenured Full Professor Anna Volkova: School of Engineering: Department of Energy Technology: Smart District Heating Systems and Integrated Assessment Analysis of Greenhouse Gases Emissions
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 Computer Systems focuses on the areas of design of dependable computing systems, including reliability, verification and testing of nanoelectronic systems, intelligent and control systems, virtual reality, and biorobotics. The department is highly engaged in International cooperation in research, and teaching provided through all study levels – bachelor’s, master’s and doctoral studies.
For information about the admission process, please visit the PhD Admission homepage
Applications can be submitted from 01.06.2026 to 30.06.2026
