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Field
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interdisciplinary teams to lead projects aimed at advancing flexible electronics for real-time physiological monitoring/wearable robotics, contributing to the development of next-generation healthcare solutions. Key
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interdisciplinary teams to lead projects aimed at advancing flexible electronics for real-time physiological monitoring/wearable robotics, contributing to the development of next-generation healthcare solutions. Key
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-making algorithm for autonomous vehicles. The work will involve sensor fusion, perception, trajectory prediction and test rig set-up, and experimental validation. Job Requirements: PhD Degree in Vehicle
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field experiments and system validation for UAV swarm behaviours. Prepare technical reports and contribute to publications and funding proposals. Job Requirements: PhD in Electrical Engineering, Aerospace
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of nuclear and radiation engineering, including imaging, robotics, high-performance computing, reactor design and materials development. The groups provide a supportive community striving to solve the most
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 15 days ago
-supervision at MPhil and PhD levels. The Research Fellow will participate in industry and open-source projects alongside academic colleagues in the CIICADA Lab. They will engage in joint research with chief
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. Responsibilities will include contributing to student mentorship at undergraduate and master's levels and co-supervision at MPhil and PhD levels. The Research Fellow will participate in industry and open-source
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automation of organic and inorganic self-assembling systems . The successful candidate will have a PhD in either inorganic or materials chemistry with some experience of Programming, computer science and
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synthesis by self-assembly and mechanistic analysis. The successful candidate will have a PhD in either inorganic or materials chemistry with some experience of analytics for mechanistic analysis
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. Job Requirements: Preferably PhD in Computer Science or related field. Background and familiarity with the implementation and deployment of machine learning pipelines in embedded systems (e.g., robotic