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, supp...