346 web-developer-"https:"-"https:"-"https:"-"Fraunhofer-Gesellschaft" positions at Monash University
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. The successful candidate will bring a strong record of tertiary teaching, curriculum and subject development, and scholarly research activity, with evidence of refereed publications and the ability to contribute
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these issues is critical for building trustworthy multimodal AI systems. Research Objectives The goal of this PhD project is to develop scalable Bayesian uncertainty estimation frameworks for single- and multi
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a unique opportunity to influence and develop the Faculty’s curriculum and research training programs, thereby shaping the future of educational research and practice. This position demands a special
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This project focuses on developing algorithms capable of automatically identifying and categorizing mobile ringtones. This involves leveraging machine learning techniques to analyze audio signals
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the brain. This wouldn't be a typical machine learning PhD, as many aspects can only be examined on a philosophical and theoretical level. There may be scope to implement aspects in the ideas you develop
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A research-only academic is expected to contribute to the University’s research effort while developing their research expertise through the pursuit of defined research projects relevant
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internal and external partners to support the ongoing management and development of the University’s property assets. This opportunity will suit a candidate with experience in retail, commercial or mixed-use
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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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be considered for the Monash International Leadership Scholarship. As a scholarship recipient you will receive a 100% tuition sponsorship for the duration of your degree, and the opportunity to develop
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Minimum Message Length (MML) is an elegant information-theoretic framework for statistical inference and model selection developed by Chris Wallace and colleagues. The fundamental insight of MML is