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. Knowledge of data-driven analytics, machine learning, signal processing, or advanced modelling techniques relevant to power systems. Experience with real-time simulation platforms, hardware-in-the-loop
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. Explore different approaches to building data-driven models to simulate farming systems to generate holistic indicators of systems performance, moving beyond simple indicators of productivity. Apply models
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data-driven modelling methods for renewable energy integration. Research Outputs and Funding: Publish high-quality research outcomes, contribute to milestone reports, collaborate on competitive research
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vibrant St Lucia campus About This Opportunity We are seeking a motivated and driven Postdoctoral Research Fellow to join our world-leading Queensland Brain Institute. In this research-focused role , you
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to develop innovative analytical solutions, enhance research capability, and help create lasting impact through data-driven discovery. At UQ, you will be challenged, inspired and rewarded while contributing
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focused on the development and application of recycled and waste-derived materials for advanced engineering systems. The role will emphasise computational modelling, simulation, and data-driven approaches
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School of Civil Engineering – Faculty of EAIT Full-time (100%), fixed-term position for 12 months Base salary will be in the range $85,372.89 - $113,659.72 + 17% super (Academic Level A) Visa
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Title: Research Fellow in Power Systems Engineering Faculty of Engineering, Architecture and Information Technology / School of Electrical Engineering and Computer Science Full-time fixed-term
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
Selection Criteria: PD&PEWER_Academic B_Research Fellow.pdf About the opportunity This position is a fixed term for a period of up to 12 months to play a major role in building a model of hydrogen
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
knowledge in at least one of the following areas will be highly regarded: the technology of large language models, natural language and text processing, qualitative and quantitative methods for evaluating