486 affective-computing-"https:"-"https:"-"https:"-"UCL" positions at Monash University
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% employer superannuation) Amplify your impact at a world top 50 University Join our inclusive, collaborative community Be surrounded by extraordinary ideas - and the people who discover them The Opportunity
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. Wallace", Computer Journal, Vol. 51, No. 5 (Sept. 2008) [Christopher Stewart WALLACE (1933-2004) memorial special issue [and front cover and back cover]], pp523-560 (and here). www.doi.org: 10.1093/comjnl
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: $96,768 - $104,450 pa HEW Level 06 (plus 17% employer superannuation) Amplify your impact supporting students at a world top 50 university Join our inclusive and collaborative Student Services community
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analysis, contextual analysis, audio feature extraction, and machine learning models to identify and assess potentially dangerous content. Similarly, computer vision models are implemented to analyse images
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their performance evaluated in terms of classification accuracy, computational speed, and overall usability. Required knowledge Deep learning (CNNs, Transformers) and computer vision Knowledge distillation for model
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Remuneration: Pro-rata of $83,424 - $95,825 pa HEW Level 05 (plus 17% employer superannuation) Make a meaningful impact supporting rural medical education at a world top 50 university Join an inclusive and
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appointment Remuneration: $83,280 - $113,025 pa Level A (plus 17% employer superannuation) Amplify your impact at a world top 50 University Join our inclusive, collaborative community Be surrounded by
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- $113,025 pa Level A plus 17% employer superannuation Amplify your impact at a world top 50 University Join our inclusive, collaborative community Be surrounded by extraordinary ideas - and the people who
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opportunity to make an impact and solve real world issues in a stimulating, supportive environment with access to cutting edge facilities. The Opportunity Expressions of interest are sought from outstanding
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privacy-enhancing techniques such as secure multi-party computation, homomorphic encryption, differential privacy, and trusted execution to design algorithms and protocols to secure ML models within