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university research into commercial outcomes. Under this program, PhD students will gain unique skills to focus on impact-driven research. This Project aims to develop a predictive machine learning model
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Martin Australia invite applications for a project under this program, exploring the development of Physics Informed Neural Networks (PINNs) for efficient signal modelling in areas such as weather
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of the STAMP RSV Program, supported by the Stan Perron Charitable Foundation. The PhD candidate will play an important role in developing models of RSV transmission and vaccination efficacy to inform
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on optimisation techniques and differential equation discretisation schemes will be developed for solving the developed mathematical models. Computer codes in Python/Matlab/R implementing these models and numerical
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computational methods for modelling social dilemmas that can account for real-world complexity in agents’ behaviour. We will build on novel computational techniques to produce realistic enough models that can be
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would develop new computational methods for modelling Groupthink that can account for real-world complexity in agents’ behaviour, and build realistic enough models that can fit past and present empirical
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systems and swarm robotics. The project builds on well established computational and mathematical modelling techniques to achieve its aims. Departure points will be agent-based simulations, optimisation
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Undertake testing on new AI models and systems to ensure reliability and alignment with experimental goals. Liaise with other engineers and stakeholders to finalise AI models and systems for use in
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of Machine Learning (ML) models across large-scale distributed systems. Leveraging advanced AI and distributed computing strategies, this project focuses on deploying ML models on real-world distributed
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of detailed modelling reports, skilled in contributing to and managing the production of both narrative and data tertiary qualifications – mathematics/biostatistics/computer science/engineering required