663 postdoc-in-distributed-systems-and-controls positions at Monash University in Australia
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Project Description Recent advances in mixed reality (MR) technology, which seamlessly blend the physical environment with computer-generated content around the user, have reduced the barriers
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(AI) technologies have advanced significantly, current software engineering methodologies remain insufficiently equipped to integrate responsible, reliable, and scalable AI-driven solutions into systems
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Tim McArdle Memorial Grant Achieving Potential Support Scholarship Created in memory of West Gippsland doctor, Tim McArdle, this grant is awarded to West Gippsland applicants who are interested in
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(Research) - 2-year fixed-term appointment Remuneration: AUD 36,063 per annum (tax-free stipend) Monash Engineering is a leading global centre for research consistently placing highly in international ranking
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(Research) - 2-year fixed-term appointment Remuneration: AUD 36,063 per annum (tax-free stipend) Monash Engineering is a leading global centre for research consistently placing highly in international ranking
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of Information Technology is seeking a Level A Research Fellow to join the Department of Data Science and Artificial Intelligence (DSAI). This role offers an exciting opportunity to contribute to cutting-edge research
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methods, the goal is to enhance the ability to identify and mitigate the risks posed by fraudulent online platforms. Required knowledge Python programming Machine learning background Text analysis Image
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the physics major Periodically driven many-body systems Research Profile For further details or alternative project arrangements, please contact: anna.phillips@monash.edu.
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The Victorian Bar Foundation Scholarship Achieving Potential Scholarship for Distinction This scholarship is to provide financial support to academically bright Bachelor of Laws (LLB) students from
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to real-life data. The goal is to generate new knowledge in the field of time series anomaly detection [1,2] through the invention of methods that effectively learn to generalise patterns of normal from