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, workflow managers (e.g. NextFlow, SnakeMake, etc), statistical analysis and data visualization. Familiarity with data science and machine learning. Familiarity with cellular and molecular biology, such as
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development and this may include aspects of machine learning, artificial intelligence, and data science. The ideal candidate will possess strong programming skills in R or Python, excellent communication
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. You will have extensive experience analysing longitudinal data (e.g., risk prediction, machine learning etc), an established international publication record and success in attaining research funding
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Engineering Demonstrated knowledge and experience of the Quantitative and Qualitative Research Methods, Software Design Methods for Secure AI-Enabled Software, Applied Machine Learning, Natural
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collaborator with the aim to use machine learning/AI approaches in combination with commercial multispectral and hyperspectral remote sensing platforms to perform within-field mapping of weeds and disease in
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arrangements can be negotiated with the right candidate. Be part of the Australian Institute for Machine Learning – the largest computer vision and machine learning research group in Australia – and contribute
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within the Australian Centre for Robotics (ACFR ) at the University of Sydney. The ACFR is one of Australia's leading robotics research groups, and the Robotic Imaging Lab is focused on endowing machines
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including Linux and specialist bioinformatics software Knowledge and experience of pangenomics or machine learning applications is desirable About your application Full details of the position's
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in the use of Python and/or C++ coding for machine-learning tasks A collaborative approach to research and enjoy working in a team environment Good communication skills developed for liaising with a
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) at the University of Sydney. The ACFR is one of Australia’s leading robotics research groups, and the Robotic Imaging Lab is focused on endowing machines with new ways of seeing the world. We are expanding our team