UniSQ PhD Scholarship

The University of Southern Queensland (UniSQ) is offering a funded PhD opportunity through the Queensland-Bavaria Collaborative Research Project in Digital Twins, artificial intelligence and future energy systems. The doctoral researcher will join the Artificial Intelligence Applications Laboratory and an international research and industry team spanning UniSQ, Queensland energy-sector partners, Julius-Maximilians-Universität Würzburg and Chemnitz University of Technology.

The project will adapt an established European Digital Twin architecture for Queensland electricity-network applications. Rather than starting from scratch, the researcher will work with the Würzburg developers and the UniSQ supervisory team to transfer, extend and validate the existing modelling framework for Queensland feeders, transformers and zone substations.

Research activities may include creating spatially resolved inventories of distributed energy resources and flexible demand, including rooftop photovoltaic systems, electric-vehicle charging, batteries, heat pumps, air-conditioning loads and other flexible consumption. The project will also integrate distribution-system simulation tools such as OpenDSS and develop aggregated virtual-device models for photovoltaic systems, EV fleets, batteries and flexible loads.

A major research component will involve AI and machine-learning methods for short-horizon solar-generation, residual-load and demand nowcasting. These forecasts will be integrated into feeder-level Digital Twin workflows to assess demand response, flexibility services, market scenarios and spatially targeted activation strategies. The researcher will validate the adapted platform against Queensland transformer, feeder and zone-substation observations, including inverter and power-quality considerations.

The PhD combines computational research, power-system modelling, renewable-energy integration, forecasting, optimisation and decision support. It will involve comparative Queensland-Bavaria analysis and contribute to an industry-ready Digital Twin platform, validated DER and flexible-demand datasets, operational forecasting tools and practical pathways for future energy-system scale-up.

The candidate will receive multidisciplinary supervision and mentoring, direct knowledge transfer from the German research team, exposure to real industry data and opportunities to engage with engineers, international academics and energy-sector partners. The research environment is designed for an ambitious researcher interested in connecting AI and computational methods with physical electricity-network processes and practical renewable-energy outcomes.