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project focused on developing a digital twin of a poultry breeding program. The project aims to investigate novel prediction technologies and alternative breeding program designs to create and prioritise
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, plant, livestock, and atmosphere processes. In this role, you will support the development and delivery of predictions of wood products, carbon and water use for the Australian forest plantation sector
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studies. Manage technical assistance to sample and analysis field trials. Develop trait predictions for high throughput phenotyping analysis. Produces quality research outputs consistent with discipline
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 1 month ago
, along with experience using high-performance computing environments, is required, and experience using structure prediction or AI methods is desirable. Working at ANU This is an opportunity to work with a
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biomolecules. Proficiency in programming or scripting for simulation and data analysis, along with experience using high-performance computing environments, is required, and experience using structure prediction
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Institute) Topic: To provide robust, Australian evidence to examine the yet unknown potential benefits and harms on pregnancy, birth, and infancy in Victoria, translating findings into predictions of current
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to lead to improved predictive design of biomass crops for the production of sustainable aviation fuel. The postdoc will also co-supervise PhD students and Honours students. To be successful you will need
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approaches to model uncertainty for learned computer vision systems, including dense prediction. The position will develop novel methods for deep learning in computer vision that accurately quantify their own
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technologies (e.g., 5G) for industrial automation and remote operations. Track record of successful competitive grant applications. Experience in postgraduate student supervision. Experience with predictive
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forecasts of soil moisture and plant traits. Your work will be critical in delivering an early warning system for bushfire risk, enhancing the ability to predict and manage these natural disasters