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Scholarship in CSIRO Industry PhD Program - Project 1: Resilient & Practical Quantum-Safe Threshold Cryptography Job No.: 678541 Location: Clayton campus Employment type: Full-time Duration: 4-year
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Scholarship in CSIRO Industry PhD Program - Project 2: Techniques and Frameworks for Enabling Post-Quantum Cryptography (PQC) Migration Job No.: 678538 Location: Clayton campus Employment Type: Full
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PhD Program – Understanding core competencies and mechanisms in the development and prevention of problem behaviour and poor mental health in the adolescent and early adult years Job No.: 680087
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to devise adaptive models that take into account the dynamically changing characteristics of environments and detect anomalies in ‘evolving’ data. Over the last two decades, many algorithms have been proposed
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The world is dynamic and in a constant state of flux, yet most machine learning models learn static models from a dataset that represents a single snapshot in time. My group's research is
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, software, human-computer interaction, ...). We also work very much interdisciplinarily with colleagues from other faculties, e.g. on bio-diversity matters, on physical aspects, on modelling aspects, and on
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Industry Innovation Program Scholarship The Embedded Co-Op Scholarship funded by an Industry Partner via the corresponding Faculty be introduced to allow industry and students to directly interact
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offers a dynamic, interdisciplinary environment focused on solving pressing urban mobility and infrastructure challenges. You will have access to: Dedicated research computing and storage infrastructure
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: 04EX783), pp439-444 Frey and Osborne (2013) Frey and Osborne (2017) P. J. Tan and D. L. Dowe (2003). MML Inference of Decision Graphs with Multi-Way Joins and Dynamic Attributes , Proc. 16th Australian
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The world is dynamic, in constant flux. However, machine learning typically learns static models from historical data. As the world changes, these models decline in performance, sometimes