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This project is aimed at developing platforms to provide security in the post-quantum era. The project aims to incorporate quantum technologies, including quantum machine learning and quantum
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The successful applicant will conduct research to design and develop novel machine/deep learning based trust technologies for securing IoT services/devices. The successful applicant will conduct
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The proposed PhD project aims to build a machine learning/deep learning-based decision support system that provides recommendations on precision medicine for paediatric brain cancer patients based
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initiatives across the STEM college, and fostering a multidisciplinary approach to partnerships. Additionally, they assist in organizing Work-Integrated Learning (WIL) events for partners, create marketing
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the energy market, Role of EVs in the grid, Power System Stability Analysis Using Machine Learning Techniques and more. Eligibility Requirements: Applicants must be Australian citizens or Permanent Residents
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of 17% superannuation applies. Two fixed-term, full-time positions available for 2 years. An exciting opportunity for two passionate Machine Learning Engineers to drive cutting-edge research and real
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Levin Kuhlmann Research area Machine Learning We are seeking a highly motivated and innovative PhD student interested in exploring the opportunities for using AI to enhance personalisation of services and
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This scholarship aims to develop practical methods for optimisaton in large supply chain operations. Ideally candidates should have strong AI, machine learning, and optimisation backgrounds
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Application dates Applications close30 May 2025 What you'll receive You'll receive a stipend of $41,600 per annum for a maximum duration of 3.5 years while undertaking a QUT PhD. The duration
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models like SWMM are computationally slow and lack scalability, while opaque AI methods risk biased outcomes. This project addresses these gaps by developing a responsible machine-learning framework