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environments. Access proprietary market data and institutional infrastructure, including cutting-edge computing environments and live market simulation tools. Translate research into production, contributing
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interested in connecting spatial and spectral information to understand complex materials systems at the molecular level with machine learning. PhD Student A will work with tumour sections to develop multiple
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to: Multimorbidity post-sepsis Health literacy and navigation in post-sepsis care Digital tools and technology-enabled follow-up Nurse-led models of care The successful candidate will join a dynamic and collaborative
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Lazzarini as your proposed principal supervisor, and copy the link to this scholarship web page into question two of the financial details section. About the scholarship Diabetes is the most rapidly growing
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children. Mechanistic modelling of disease transmission involves the use of computer code to represent the epidemic dynamics of infectious disease spread within the community. This allows modellers
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to integrate floating offshore wind turbines and wave energy converters to capture wind and wave energy. The hybrid system’s dynamic responses will be investigated and advanced vibration control technologies
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are available in the School of Molecular and Life Sciences at Curtin University, Australia. The successful candidates will work with Associate Professor Guohua Jia on his Australian Research Council (ARC) Future
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underwater communications is necessary. Conventional approaches in underwater communications only develop fixed models based on human knowledge or understanding which cannot fully cover the highly dynamic and
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dynamics model that incorporates a range of net zero technologies and lifestyle solutions along with social and political drivers and barriers to adoption. By doing so, it will guide and enable policymakers
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models