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Australian National University | Canberra, Australian Capital Territory | Australia | about 2 months ago
research and deliver a research-led education program that encompasses the breadth of the sciences, supported by extensive international networks and by world-class facilities. To find out more about the
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quantitative methodologies, especially for instance in the areas of econometrics (microeconometrics), statistics, and economic modelling. Demonstrated knowledge of statistical and econometrics software packages
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the development of high-quality, productivity-driven research networks across RMIT and with local and national, internal and external partners. You will be expected to engage in high quality research projects
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server infrastructure. Collaborative software development using tools such as git/GitHub or equivalent. Highly desirable: experience with generative representation learning models (VAEs, GANs, etc
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publications. It involves conducting systematic reviews and working with analytical software such as R or Python, with a focus on both independent and collaborative research. The role also requires experience in
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data to better understand the structure and function of the brain—across health, disease, and species. You'll work with world-class researchers in fields including network science, biophysical modelling
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experience with analytical software like Stata, R, or Python, are essential. The ideal candidate will also have experience with geospatial tools. Additionally, you will have experience designing and managing
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and immunotherapy research, a well-established clinical trial network, and recognised leadership in producing industry-ready graduates. With in-depth expertise and extensive experience in AI and machine
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, Software Engineering, or a related field. Demonstrated experience in deep learning and large language model research, joint modality representation learning, knowledge graph construction, particularly in
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areas. You will embed your research expertise into the life of the School through the development of high-quality, productivity-driven research networks across RMIT and with local and national, internal