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computer vision and machine learning research group in Australia -- and contribute to world-leading research projects at the CommBank Centre for Foundational AI This postdoctoral research position is part of
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collaboration between industry, government, and academia. The Australian Institute for Machine Learning (AIML) at the University of Adelaide is the largest computer vision and machine learning research group in
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talent in our workforce. The creation of a diverse and inclusive workforce is central to our ability to unlock potential, enhance our success, and achieving our vision of being in the Top 1
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Australian National University | Canberra, Australian Capital Territory | Australia | about 1 month ago
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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university, our vision is for people and technology working together to build a better world. Central to our vision is our commitment to diversity, equity, and inclusion. We pride ourselves on being an equal
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Sydney University. The vision for the Institute is to optimise human interaction, and wellbeing across lifespan. We strive to solve the problems that matter most through the themes: sensing and perceiving
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Swinburne has become a world-class university, driving social and economic impacts through science, technology, and innovation. As a dual-sector university, our vision is for people and technology working
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and pursue innovative scientific ideas. A strong vision for your contribution to the project, aligned with its overarching goals, will be essential. The successful candidate will thrive in a
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Employment Flinders University is proud to be an organisation that is committed to our Reconciliation Action Plan and Indigenous Workforce Strategy. Our vision is to be a preferred employer for Aboriginal and
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until 30 June 2027 to contribute to the development of artificial intelligence systems which reliably and effectively detect anomalies in the workspace based on vision and/or natural language modalities