381 machine-learning "https:" "https:" "https:" "https:" "https:" "Bournemouth University" positions at Monash University
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will receive a Research Living Allowance, at current value of $37,145AUD per annum 2026 full-time rate (tax-free stipend), indexed plus allowances as per RTP stipend scholarship conditions at: https
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Project description: Nowadays, data-driven machine learning algorithms are well suited to solve real-world problems that require high-level prediction accuracy. However, it seems as if nothing beats
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they fulfil the criteria for Masters by Research & PhD admission at Monash University. Details of the relevant requirements are available at https://www.monash.edu/engineering/future-students/graduate-research
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contrastive self-supervised learning task to learn from massive amounts of EEG data. Frontiers in human neuroscience. [2] https://www.emotiv.com
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This project aims to employ advanced machine learning techniques to analyse text, audio, images, and videos for signs of harmful behaviour. Natural language processing algorithms are utilized
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networks that can be trained to do machine learning and AI tasks in a similar way to artificial neural networks. In this project you will develop machine learning theory that is consistent with the learning
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Information Technology Industry-Based Learning Placement Grant This grant is awarded to high achieving students in the Faculty of Information Technology who undertake a placement with an industry
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encoded in computer software and can be used as decision support systems (DSS). These may be used by decision-makers with different domains of expertise than the analysts who built the DSS system. Therefore
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We are seeking a motivated PhD candidate to work on unsupervised music emotion tagging within the broader field of affective computing. The project aims to develop reproducible machine learning
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are: Rapidly identify AMR and predict treatment responses through use of genomics and machine learning in a clinical context Detect healthcare-associated transmission of AMR in real-time and transform outbreak