664 information-security-"https:"-"https:"-"https:"-"https:"-"https:"-"CUBO"-"CUBO" positions at Monash University in Australia
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background in AI/ML, data science, or signal processing Interest in music informatics, emotion modelling, or multimodal AI Ability to implement and evaluate machine learning models independently Commitment
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Discovery Project, this research aims to develop highly novel physics-informed deep learning methods for Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) and applications in image
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Antimicrobial resistance (AMR) is one of the most significant and immediate threats to health in Australia and globally. As an Infectious Diseases physician and researcher, the second supervisor is
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AI is now trending, and impacting diverse application domains beyond IT, from education (chatGPT) to natural sciences (protein analysis) to social media. This PhD research focuses on the fusing AI
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. More recent advances include the increasing emergence of driverless cars. This is an area in which rapid changes continue to occur, most recently as the world both deals with and looks to emerge from
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operational needs. There is a key opportunity for specialist work in an emergent intersection area which we can call Value-Based Digital Health (VBDH). To expand further, VBDH is a discipline area that sits
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While deep learning has shown remarkable performance in medical imaging benchmarks, translating these results to real-world clinical deployment remains challenging. Models trained on data from one
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team shaping national policy As Research Fellow, turn big data into real-world impact The Opportunity The National Addiction and Mental Health Surveillance Unit (NAMHSU) is leading a Medical Research
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I supervise a wide range of projects in stellar astrophysics, with a focus on low and intermediate-mass stars, which have masses similar to or slightly larger than our Sun. This work is carried out
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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