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project in which their data was used. This project will establish the requirements for, and develop, a new class of participatory research data governance architecture that will address the research-centric
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that unveil the unseen. analyse complex data to uncover insights and pioneer new strategies for molecular sensing and imaging. We’re looking for someone who brings: A PhD in chemical biology, organic chemistry
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, software architectures, Machine Learning
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This PhD project is part of a larger project that aims to explain the uncertainty of Machine Learning (ML) predictions. To this effect, we must quantify uncertainty, devise algorithms that explain
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guide colleagues, you foster a collaborative environment that advances both professional and academic standards. While your primary expertise lies in industry practice, you are committed to bridging
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refereed journals and textbooks or equivalent teaching resources Demonstrated strong record of teaching experience in a tertiary environment Demonstrated ability in undertaking outstanding research and
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at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need
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research teams Working under broad direction, the Research Fellow will help drive research outcomes and continue developing their expertise in an internationally recognised academic environment. About Monash
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communication skills, as well as a proven ability to progress matters and lead teams in high-pressure, high-volume environments. Your strategic mindset, operational leadership, and commitment to ethical practice
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: Experience in physical system modelling including finite element modelling Experience working with large codebases in open source software environments Proficient user of HPC environments including MPI