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machine learning techniques for building efficient reduced-order models in the context of the numerical simulation of parameterized partial differential equations. The analysis of recent deep learning
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learning community where young people see aspects of their backgrounds and identities reflected around them, where they feel a deep sense of belonging, and where they discover and use their voices to full
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Neutral Infrastructure (dfCO2), this role contributes to Program 4: Machine Learning for Carbon Performance (https://dfco2.org.au/program_4 ) that aims to advance the next‑generation AI methods to model
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synchronous sessions, workshops, practical sessions, project supervision, clinics, tutorials, seminars, and the creation of blended learning materials. Context The Department of Computer Science
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the use of effective applications and services for teaching and learning. We drive a culture that is forward-looking. With a strong passion for IT, our people are always striving to improve, push boundaries
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: Experience in deep learning, machine learning and medical imaging processing Programming experience: Python, MATLAB, SPSS, Shell. Experience in working with Linux workstation. Excellent verbal and written
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learning, and deep learning applied to biomedical research and medicine. • Experience in biology or biomedical research projects. • Experience with Linux, R, and Pandas. • System and server administration
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the following areas: deep learning, reinforcement learning, imitation learning, robot perception, navigation, and manipulation. Experience with whole-body control, humanoid or multi-DOF platforms, and
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application of machine and deep learning models. The balance between experimental and computational method development will depend on the candidates’ profiles. Start date is expected to be 1 June 2026 or as
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custodians of the land, sea and waters of the areas upon which we live and work. We recognise their valuable contributions and deep connection to country and pay respect to Elders past and present. Fellow of