198 machine-learning "https:" "https:" "https:" "https:" "https:" "University of St" Fellowship positions in Australia
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, please contact Dani, v ia talentsupport@rmit.edu.au or visit our Careers page for more contact information - https://www.rmit.edu.au/careers . We are a Circle Back Initiative Employer – we commit
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. This pivotal role sits at the heart of the Strategic Alliance between Open Door and Military and Emergency Services Health Australia (MESHA), strengthening national research capability and driving meaningful
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 8 days ago
machine-learning methods to investigate the deep-time controls on copper mineralisation. The role will involve developing reproducible computational workflows, generating predictive maps of copper
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postgraduate qualification in Data Science / Computer Science (PhD preferred) Strong expertise in Python and/or R, SQL, data engineering and machine learning Experience with EMR systems (Cerner highly desirable
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systems (such as RedCAP), Endnote files, and databases Demonstrated experience with data analysis, visualization, and building machine learning models in programming language such as Python or/and R
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developing research projects and reporting against milestones. Experience working with a range of computer systems and applications, including referencing software (e.g. EndNote), survey platforms and high
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Earth Engine, ENVI, MATLAB, or R. Desirable Proficiency in applying machine learning methods to multispectral and hyperspectral data for detecting crop diseases and estimating crop yield and quality
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completion) in computer science, electrical engineering, AI, machine learning, remote sensing, robotics, or a closely related discipline. Demonstrated expertise and research track record in deep learning and
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Innovations Group seeks a forward‑thinking expert in statistical machine learning to translate complex biological datasets into actionable AI‑driven insights. You will enhance genomic selection and breeding
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experience in using statistical and mathematical tools to analyse and interpret soil data, spatial modelling, multivariate statistics and/or machine learning, and relevant coding languages (e.g. R, Python