PhD Student in AI and Digital Twins for Resilient Energy Systems
Core
Developing AI methods and digital twins for resilient district heating, cooling, and building energy systems under uncertainty.
Role type
PhD researcher in AI and digital twins for energy systems
Builds
Digital twins mirroring energy system behavior in real time; AI/ML models for monitoring and forecasting
Domain
Energy systems (district heating/cooling, building energy) + Artificial Intelligence
Deliverable
production ML models | research
Required skills
machine learning, modelling and simulation, Python programming, physics-based modelling, data-driven approaches
Preferred skills
deep learning, time-series data analysis, sensor data processing, optimisation, control, uncertainty quantification, scientific publishing
Technologies
PyTorch, TensorFlow
Responsibilities
Develop AI/ML methods for modelling and forecasting; Build and validate real-time digital twins; Investigate resilience improvements against disturbances; Combine physics-based and data-driven models; Validate methods on real data with partners; Publish research results
Seniority
PhD candidate (researcher)
