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to leverage machine learning approaches for the optimization of polymer properties and degradation profiles. The successful candidate will lead pioneering research in controlled polymer synthesis, employing
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, measure transport and machine learning on developing a novel mathematical framework for identifying reduced dynamical models of high-dimensional complex multi-scale systems. The project will develop fast
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monitoring approaches (e.g., machine learning, real-time sensing). Sponsorship / work rights for Australia You must have unrestricted work rights in Australia for the duration of this employment to be eligible
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density) influence energy dissipation develop mathematical models to predict and explain these effects collect and analyse data, including with the use of machine learning use this knowledge to design
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. Essential qualifications and experience a PhD (or near completion) in one of the following fields (or a closely related discipline): Computer Science, Artificial Intelligence, or Machine Learning Economics or
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related discipline): Computer Science, Artificial Intelligence, or Machine Learning Economics or Econometrics (particularly applied micro, behavioural, or decision-focused modelling) Applied Mathematics
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across large cohorts (e.g., >300 samples) demonstrated experience in data analysis, visualisation, and machine learning, using programming languages such as R or Python excellent interpersonal, verbal, and
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monitoring approaches (e.g., machine learning, real-time sensing). Sponsorship / work rights forAustralia You must have unrestricted work rights in Australia for the duration of this employment to be eligible
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students within the discipline, fostering a supportive and collaborative learning environment. Contribute to administrative activities and actively participate in School and Faculty meetings and initiatives
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related discipline): Computer Science, Artificial Intelligence, or Machine Learning Economics or Econometrics (particularly applied micro, behavioural, or decision-focused modelling) Applied Mathematics