201 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "The Institute for Data" positions at The University of Queensland in Australia
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provide mentoring and peer coaching opportunities, that provide lifelong beneficial partnerships. To learn more about philanthropy at UQ visit https://alumni.uq.edu.au/giving/ . To learn more about
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, skills, ideas and provide mentoring and peer coaching opportunities, that provide lifelong beneficial partnerships. To learn more about philanthropy at UQ visit https://alumni.uq.edu.au/giving/ . To learn
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. In this unique role, you will work closely with data scientists, a bioinformatician and a machine learning specialist to construct custom workflows that allow the high-throughput processing and
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internal service roles and committees, perform administrative functions, provide support to colleagues, and uphold university values. This is a research focused position. Further information can be found by
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agreements with the TERN Office. A key strategic objective is to sustain TERN into the longer term. Information about TERN Australia is available at http://www.tern.org.au . Join a community where excellence
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analytics More information can be found at https://smi.uq.edu.au/mishc About This Opportunity This is an exciting opportunity for a Professorial Research Fellow to work within the Minerals Industry Safety and
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. This involves supporting the supervision of students during rural clinical placements, facilitating interprofessional learning opportunities during student placements, and enhancing support and education to both
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provide coverage for academic roles as needed. These are teaching focused positions. Further information can be found by viewing UQ’s Criteria for Academic Performance . About UQ As part of the UQ community
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role in delivering high-quality, practice-based learning. You will provide operational, technical and instructional support across livestock practical classes, while working closely with the Livestock
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machine learning, big-data analytics, and data-driven approaches to optimise composition–process–property relationships. Key responsibilities will include: Research: Conduct additive manufacturing research