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the development of new algorithms for processing, analysis and inversion of active and passive seismic data and the application of these algorithms to field data. Student type Future Students Faculties and centres
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algorithms and deep learning models. Have proficiency in Python in a Linux environment and development experience using Tensorflow or PyTorch. Have strong linear algebra and computer vision knowledge. Have
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are working with a PhD student and a research fellow who have collected underwater data and preliminary algorithms. With their guidance and supervision, project aims and objectives are expected to be achieved
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research translation and industry engagement through the development and application of digital, robotics and sensor-based technology to address key challenges in ageing, which are to enhance cognition
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(University of Adelaide). Project 1.4. Quantum biosensor development (University of Adelaide). Project 1.5. Quantum chemical sensor development (University of Adelaide). Project 2.1. Superconducting quantum
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and polyploid crop species and benchmark them against other methods such as graph-based methods. This project will combine algorithm development and computational programming with large population
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optimization. -- Identify factors that contribute to the robustness of training algorithms against local minima and explore potential improvements. • Objective 4: Architectural Designs -- Evaluate the impact of
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catheter probe based magnetic sensors for biological applications. To perform quantum sensing, we optically read-out the NV centre's electron spin state to quantify the perturbing effect of nearby