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genetic variation into elite germplasm. This will require the integration and optimization of several technologies, including genomics, machine learning, genetic simulation, and speed breeding. This is a
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of student placements and work-integrated learning (WIL) within the School. You will work closely with the WIL Placement Coordinator and Academic Course Coordinators to support student placement activities
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reputed refereed journals and presenting at conferences. Technical expertise in programming (e.g. python) and experience with high-performance computing are highly desirable. Experience in machine learning
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to engage with multidisciplinary teams and external partners. Desirable attributes include experience with spatio-temporal models, machine learning, Bayesian methods, and knowledge of environmental exposure
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closely with researchers and developers, you will help identify challenges, propose solutions, and improve user interfaces based on feedback and testing. You can learn more about the Australian Internet
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Change Makers. Together we create opportunities to share knowledge, skills, ideas and provide mentoring and peer coaching opportunities, that provide lifelong beneficial partnerships. To learn more about
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sound judgement and discretion. Excellent computer skills including proficiency in Microsoft Office and the ability to rapidly acquire skills in new applications. Experience using University systems
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in conducting human behavioural experiments or human-centered field studies. Demonstrated experience in using machine learning, such as deep learning for image processing, or natural
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experience in machine and/or deep learning applied to geospatial data. Demonstrated experience in the use of HPC and handling of very large datasets. Experience in software development and contribution
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the application of AI and machine learning to identify novel therapeutic targets and advance precision medicine approaches. For further information, please click here to view the full appointment