381 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Mines Paris PSL" positions at Monash University in Australia
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. For more information on the work we do, please visit our website: http://www.monash.edu/vpfinance The Opportunity The Strategic Sourcing Manager position is an important role that drives the development and
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the work we do, please visit our website: http://www.monash.edu/vpfinance The Opportunity The Category Manager position is an important role within Strategic Procurement, responsible for shaping
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new approaches for a playful human-computer integration future. For more information see http://exertiongameslab.org The result will be a thesis in the field of interaction design.
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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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Levin Kuhlmann Research area Machine Learning We are seeking a highly motivated and innovative PhD student interested in exploring the opportunities for using AI to enhance personalisation of services and
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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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"A picture is worth a thousands words"... or so the saying goes. How much information can we extract from an image of an insect on a flower? What species is the insect? What species is the flower? Where was the photograph taken? And at what time of the year? What time of the day? What was the...
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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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requirements are available at https://www.monash.edu/engineering/future-students/graduate-research/how-to-apply Your application will be looked upon favourably if you: Graduated in the top 10% of your year level
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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