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demonstrated ability to communicate and interact with a diverse range of stakeholders and students. Demonstrated knowledge in Quasi-Monte Carlo methods and/or finite element analysis and/or machine learning is
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Educational Technology in the School of Computer and Mathematical Sciences. The successful candidate will be a researcher in the use of technology to support cognitive and meta-cognitive skills of students
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applies. 2 Fixed-term position available for up to 24 months. Flexible work arrangements can be negotiated with the right candidate. Be part of the Australian Institute for Machine Learning -- the largest
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University policies and procedures are followed. Skills and Experience To be considered for this position, you will have demonstrated ability to learn new software together with a capacity for innovation and
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one-on-one coaching sessions as well as workshops. To be successful in this position, you’ll have / you’ll have as a minimum: Expert knowledge of a range of Adobe tools with a capacity to quickly learn
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. At the heart of the student experience are transformational opportunities in learning and teaching. The Student Experience branch within DASE is responsible for providing services that deliver an excellent
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experience. At the heart of the student experience are transformational opportunities in learning and teaching. The Student Experience branch within DASE is responsible for providing services that deliver
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publication Strong programming skills and familiarity with machine learning or finite element modelling Not currently receiving another scholarship of equal or higher value Application process Future student
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at the Australian National University. The Fellow will lead independent, high-impact research in advanced machine learning and hybrid modelling for genomics and cellular processes, contributing directly to the
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methods for provable network security. The School of Computer and Mathematical Sciences is recruiting a research fellow to work on next generation network security technologies. Join a world-class research