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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 9 days ago
of these domains can be considered. The Systems Theory and Robotics group is a world leading research group in the development of foundational theory, algorithms and technology for application to robotic system
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market mechanisms. The successful candidate will work closely with colleagues across the project to develop analytical methods, algorithms, and visualisation tools that connect IoT monitoring with broader
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: Statistical model development: Led the development of advanced statistical models and machine learning algorithms for forecasting precipitation and temperature in Morocco. This will involve data analysis, model
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learning algorithms. We combine statistical methods with online reinforcement learning algorithms to develop reinforcement learning algorithms and inferential tools. The successful applicant will be expected
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. In this position, your primary task will be to lead the development of algorithms, software, and hardware to extend the current HAUCS framework. This includes developing the sensors, sensing robotic
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development of transparent, closed-loop control system for individualized diuretic closing including the validation and advancement of machine-learning and control algorithms, building production-oriented
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learning based optimization algorithms, human and AI coordination in best decision making for urban transportation related problems. The role will focus on developing generic frameworks and innovative
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to minimize algorithmic bias. Develop expertise in evaluating AI devices that can adapt and learn post-deployment, including understanding evolving algorithms and creating methodologies to assess algorithm
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offloading strategies for IoT networks. The role will bridge rigorous theoretical work with hands-on offloading algorithm design and development for IoT networks. The core responsibility is to build and
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Fellow to conduct cutting-edge research in AI-enabled modelling and design of microwave and RF devices. The successful candidate will develop advanced physics-assisted modelling techniques to accelerate