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available for two years. Keywords: Geometric Deep Learning, in particular Graph Neural Networks, Deep Reinforcement Learning, Generative Modelling, in particular Denoising Diffusions, Combinatorial
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an opportunity for a Postdoctoral Fellow. You will contribute to UNSW’s research efforts in developing machine learning and deep learning algorithms for dynamic systems (sequential or time-series data). Experience
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Learning-related field. Programming experience in Matlab, Python, C++ or other relevant language and experience in deep neural networks. Experience and demonstratable knowledge in deep learning, transformer
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Science, Engineering or other Machine Learning-related field. Programming experience in MATLAB, Python, C++ or other relevant language and experience in deep neural networks. Experience and demonstratable knowledge in
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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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, empathy, and a deep understanding of diverse perspectives and cultures. About the Role As the Research Fellow: Aboriginal, you will be responsible for undertaking research projects in Aboriginal and Torres
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have a PhD in Computer Science (or be able to demonstrate equivalent research experience in modelling and simulation, software engineering research) and possess a deep and demonstrable knowledge
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. This Indigenous-led program serves as an academic home for Indigenous researchers and is grounded in land-based learning, research, and curriculum development. Yoonggama Ma Nga centers First Nations epistemologies
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leadership in teaching and learning. This is a Full Time, Fixed Term position available for a period of up to 5 years at the classification of Level D. The full-time equivalent salary range is $161,351.44
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Australian National University | Canberra, Australian Capital Territory | Australia | about 2 months 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