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) on physical robots. • Use evolutionary algorithms to optimize both the robot’s body and brain together. • Apply quality-diversity methods to discover a wide range of high-performing designs
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to join our team in advancing new diagnostic technologies. This project focuses on developing real-time electrochemical sensors for continuous, on-site monitoring of key analytes relevant to environmental
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and iteratively improved. • Integrate and test autonomy stacks (perception, learning, planning) on physical robots. • Use evolutionary algorithms to optimize both the robot’s body and brain
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. Develop intelligent algorithms that allow cooling systems to be controlled based on sensor inputs, ensuring adaptation to different scenarios and operating conditions; 4. Integrate and validate
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efficient numerical methods for solving the problems, and implement the numerical algorithms to verify the theoretical results. Job Requirements: Applicants should possess a PhD degree or have satisfied all
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-term position for two years is part of the Queensland Quantum Decarbonisation Alliance (QDA) — a major collaboration between leading Queensland universities and partners, including CSIRO and PsiQuantum
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basic science with biotech applications and combines rich functional assays with cutting-edge computational biology. Our method relies on aptamer-based sensors to recognise each specific metabolite target
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ultrasound sensors, a technology first pioneered by the University College London Photoacoustic Imaging Group. Their primary areas of investigation will be increasing the performance of Fabry-Perot sensors in
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vehicle (AV), allowing for automated detection, prediction, mapping, and planning. During the vehicle’s operation, data is obtained through a myriad of sensors in an AV—including RADAR, LIDAR, cameras, and
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of marine geophysical survey and machine learning algorithms; Job Requirements: A Bachelor degree in geophysics or equivalent and A PhD degree in geophysics / geomechanics or equivalent from a recognized