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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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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 19 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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. 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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an opportunity for a Postdoctoral Fellow. You will contribute to UNSW’s research efforts in developing machine learning algorithm for photovoltaic applications and utilising them for the investigation
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and planning algorithm for high-speed autonomous vehicle. The work will involve algorithm development, simulation, test rig set-up, and experimental validation. Requirements: The candidate must at least
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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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dataset from various internal and external resources. ● Understand and apply best-in-class algorithms to address biological and clinical questions. ● Collaborate effectively within an interdisciplinary team
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activities will be developed in the Power Electronics area, focusing on programming control algorithms on a Xilinx FPGA. This project aims to implement internal fault tolerance in a power electronics
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are in particular targeting development of data-driven high-performance computing techniques for unbiased discovery of generative models & theory and algorithms for network inference with special reference