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modelling and simulation of transmission and distribution networks, including benchmarking data models, developing optimal power flow algorithms, and creating state estimation and multi-energy optimisation
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novel opportunity to automate and improve the frailty assessment process, aiming for greater consistency and predictive accuracy. Aims i) Develop a deep learning algorithm to autonomously detect and
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will design quantum-safe threshold encryption and/or authentication algorithms. The expected outcome is the design of methods, techniques and their software prototype to implement quantum-safe threshold
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explore unconventional ideas, develop computer algorithms for data analysis, create new experimental approaches, and apply the technique in areas like biomedicine, materials science, and geology. My group
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algorithm that allows accurate simulation of fluid transport processes in porous media coupled with chemical reactions (e.g. dissolution and precipitation). The algorithm will be validated firstly against
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modelling, enabling more cost-efficient training algorithms. Program overview The successful candidate will receive: Admission to a PhD program at the University of Adelaide; A four-year scholarship package
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, programming, algorithms, and data analysis skills Outstanding research skills Applicants with Master degrees by research with technical publications and research experiences in structural dynamics and
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software frameworks, algorithms, robust testing and validation methods, and/or empirically validated solutions that contribute directly to social good, promoting trust, fairness, transparency, and
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the development of new algorithms for processing, analysis and inversion of active and passive seismic data and the application of these algorithms to field data. Student type Future Students Faculties and centres
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algorithms and deep learning models. Have proficiency in Python in a Linux environment and development experience using Tensorflow or PyTorch. Have strong linear algebra and computer vision knowledge. Have