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group of PhD researchers who will tackle the most pressing questions in Machine Learning while ensuring AI serves humanity responsibly. You'll work within one of our specialised research themes, each
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focus on processing and utilising machine-learning techniques to analyse large volumes of data from sensors installed in Phase 1. The aim will be to merge the QC points and tracking system developed in
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accelerate the global shift to electric mobility. This PhD will build an AI-driven fleet-scheduling framework that learns from battery data in real time and optimises charging, operations and maintenance
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. Desirable Familiarity with cloud, virtual machines or distributed computing environments. Exposure to machine-learning frameworks (e.g., TensorFlow, PyTorch). Interest in developing scalable and secure
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agricultural science with a quantitative focus (or an equivalent discipline) expertise in statistical and machine learning approaches, with the ability to apply advanced methods to complex environmental and
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staff position within a Research Infrastructure? No Offer Description Lead world-class AI research and education at RMIT. Shape the future of Generative AI, Machine Learning, and Data Science through
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(e.g., Docker, Kubernetes, cloud/edge environments). Demonstrated expertise in AI, distributed computing, machine learning, or systems software design. Strong background in software engineering
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staff position within a Research Infrastructure? No Offer Description Lead world-class AI research and education at RMIT. Shape the future of Generative AI, Machine Learning, and Data Science through
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problems Demonstrated commitment to quality assurance, compliance and continuous improvement Demonstrated experience in the use of a range of computer applications and the ability to quickly learn new
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advance knowledge and learning. For more information on our school go to the following link - https://www.unsw.edu.au/engineering/our-schools/computer-science-and-engineering Skills and Experience Research