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the low-cost and efficient production of green hydrogen lies in the development of advanced electrolyser devices based on cost-effective and high-performance catalysts and electrolytes. This project aims
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machine learning approaches show issues in model performance and efficiency and vulnerability towards the application of noise over a large number of distributed models. These issues should be overcome by
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conjunction with the ARC Centre of Excellence for Quantum Computation and Communication Technology (CQC2T), is looking for an exceptionally high-performing student to perform advanced theoretical work in
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an exceptionally high aptitude for advanced physics Candidates must have a first-class Honours degree or a Masters degree in an area of physics related to quantum technology. A Masters Degree is preferred A letter
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Autonomous methods for fault or anomaly detection and classification of PV plants with high accuracy are necessary for the monitoring of large-size PV power plants. Objectives also include other
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affect surface outcomes, benchmark against conventional techniques, and evaluate performance of the finished components. You’ll also delve into intelligent automation and machine learning to optimise
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apply for admission to QUT's Doctor of Philosophy or Master of Philosophy . The first step is to email Prof Cheng Yan providing a CV detailing your academic performance (GPA), research experience and
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resource management, or related topic with evidence of high performance is required. Advanced knowledge of quantitative and qualitative research methods is required. Relevant work experience is desirable
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modelling. This project will not only fill the knowledge gaps in developing high performance SIBs but guide the establishment of sustainable hard carbon manufacture industry.
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fabrication facilities as well as high performance computing (HPC) facilities at QUT. PhD2: Pore-network modelling of reactive transport As a PhD student, you will develop efficient pore-network modelling