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About this position
Reinforcement Learning & Battery Optimisation for the Australian National Electricity Market
Project at a Glance
Develop next-generation reinforcement-learning models that learn market-participant behaviour to deliver real-time, high-accuracy electricity-price forecasts and optimal battery-dispatch strategies. Research outcomes will be embedded in OptiGrid’s commercial platform, directly contributing to Australia’s 100 % renewable-energy transition.
Research Focus
• Machine-learning under uncertainty
• Deep-RL for sequential decision-making in energy markets
• Behavioural modelling of market participants
• Integration with real-world battery-storage control systems
Supervisory Team
• Dr Ali Pourmousavi Kani (University of Adelaide) – primary
• Prof Wen Soong (University of Adelaide)
• Dr Ehsan Abbasnejad (Monash University)
• Mr Owen Lamont (OptiGrid) – industry lead
Key Links
Full programme details & online portal:
https://scholarships.adelaide.edu.au/Scholarships/postgraduate-research/faculty-of-sciences-engineering-and-technology-set/national
General HDR enquiries: hdrindustryenq@adelaide.edu.au
Contact
Dr Ali Pourmousavi Kani – a.pourm@adelaide.edu.au
Requirement
•Strong background in reinforcement learning and probabilistic ML
•Proficiency in Python and major DL/RL frameworks (TensorFlow / PyTorch / JAX)
•Interest in energy-systems applications and cross-disciplinary collaboration
Desirable
• Experience with time-series forecasting, optimisation, or power-systems simulation
• Familiarity with cloud platforms (AWS) and large-scale data pipelines
Funding & Benefits
• AUD $52 000 per annum tax-free (2025 rate, indexed annually) – almost twice the standard Australian PhD stipend
• 4-year tenure + full tuition-fee waiver
• Industry immersion: embedded placement with OptiGrid (Lot Fourteen, Adelaide)
• Professional development: Campus Plus training in research translation & commercialisation
Eligibility
• Open to Australian citizens, permanent residents, NZ citizens, and international applicants
• Master’s or First-Class Honours in CS, Data Science, Electrical/Control Engineering, Mathematics, or related discipline
• Must meet University of Adelaide PhD admission criteria (English-language proficiency required)
• Cannot hold a prior PhD
Application date
How to apply
Prepare
• CV
• 2-page cover letter outlining alignment with the project
• Academic transcripts (certified English translation if required)
• Evidence of English proficiency (if applicable)
Submit
Email documents to Dr Ali Pourmousavi Kani
a.pourm@adelaide.edu.au
Subject: National Industry PhD Program Application – RL Energy Forecasting
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