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) and Dr Jennifer Boer (jennifer.boer@rmit.edu.au ). A copy of electronic academic transcripts A CV that includes any publications/awards and the contact details of two referees. To apply, please submit
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, with particular focus on modelling and simulation. Some example projects include: "The impact of stellar rotation on the nucleosynthesis in the first generation of stars" "Stripping of planets by
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for this scholarship and project) a copy of electronic transcripts a CV that includes any publications/awards, experience relevant to the project and the contact details for 2 referees Potential candidates should
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are recruiting four PhD students for the following Projects: Project 1: Unifying on-farm data and crop models to enhance tactical crop decisions Summary: Despite the increasing availability of on-farm data and
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describing your research interests and why you would like to undertake a PhD (maximum one page) A CV including qualifications, academic achievements, list of publications, work history and references A copy of
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); a copy of academic transcripts for all higher education qualifications; contact details of two academic referees any achievement records Enquiries: Associate Professor Ron Steinfeld Ron.Steinfeld
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to develop a PhD study in partnership with community partners and may include; Evaluating the cultural and clinical effectiveness of combination nicotine replacement therapy (cNRT) delivered via mail
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courses Any HDR student willing to use mathematical modelling and simulation in engineering and physical problems. Eligibility criteria Strong mathematical background Strong analytic abilities Experience in
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candidate must meet the minimum academic entry requirement for admission into the Doctor of Philosophy program https://research.curtin.edu.au/postgraduate/ including the English proficiency levels set by
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, the internal workings of deep neural networks remain largely mysterious, posing a significant challenge to the interpretability, reliability, and further advancement of these models. This project seeks deep