219 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" Fellowship positions in Australia
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, visit https://services.anu.edu.au/human-resources/respect-inclusion� � Application information All application documentation, including three (3) reference letters, must be submitted on MathJobs.org. In
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, Director, matt.king@utas.edu.au or 03 6226 1974. Please visit https://www.utas.edu.au/jobs/applying for our guide to applying and details on the recruitment process. For current UTAS staff, in submitting
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, rotating machines, system integration and electrical reticulation/protection. This role will also see you work collaboratively with a multidisciplinary team to advance renewable energy technology through
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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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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 10 hours 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