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. Researching and developing novel machine learning architectures for integration across multiple types of high-dimensional data. Researching and implementing novel algorithms for analysis of latent factors and
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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Developing and integrating AI algorithms into the real development progress Preparing academic publications such as patent applications and research papers Contributing to quarterly and annual report writing
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of classical and hybrid classical-quantum algorithms for treating the correlations. This position offers exciting opportunities for collaboration within UQ, across the QDA network, and with external research
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 11 days ago
: PD&PEWER Level A&B Postdoctoral - Research Fellow _ CSS.pdf This position will provide opportunities to: Conduct cutting edge research in machine learning, AI and algorithms, such as but not limited
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. The successful candidate will play a pivotal role in a project centered around variational quantum algorithm in the near-term, especially on innovating advanced error mitigation or detection techniques to solve
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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-photonic computing architectures; Silicon-photonic network architectures Machine Learning Algorithms/Systems: Experience in design and use of ML algorithms; Experience in using ML for designing computing
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technologies, ethical implications, and governance frameworks, including knowledge of algorithmic accountability and transparency. Experience with both qualitative and quantitative research methods, and