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"Practical Quantum Advantage of Reservoir Computing on NISQ Devices." Position Overview The successful candidate will conduct research in quantum and quantum-classical algorithm development, focusing
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: neuromorphic algorithms, machine learning, classifier development, AI programming Key tasks include experimental and/or computational research, collaboration within the project team, publishing results in high
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pioneering Australian research into a market-ready product for transport planning. About the opportunity Lead research and development of advanced computer vision models, multi-object tracking, and post
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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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studies, technical specifications, modelling and system design. The work to be developed involves continuous analysis of the state of the art, the definition and specification of technical and functional
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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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challenge meeting this requirement is the simultaneous need for low-power consumption. The main objective of the project is to develop a complete end-to-end high-performance DNN system for on-premise
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research in the field of AI for healthcare autonomous systems. Activities on non-healthcare systems could occasionally be requested. • Undertake research from algorithm development to real time
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, Natural Language Processing, and structured knowledge representations. As a researcher within Integreat, you will contribute to developing next-generation Machine Learning for advanced data analysis
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borehole electromagnetic data during drilling. This includes the further development and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed