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computational modelling, physics, mathematics and/or scientific programming backgrounds, ideally with experience in Earth System modelling, Earth science or related disciplines. Ideal technical requirements
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trajectory. About You You must be available to start mid-to-late 2025 Level A applicants are expected to have: A PhD (or be near completion) in engineering, applied mathematics, or computer science. Experience
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Bachelor or Master’s degree in a relevant field (e.g., transportation, urban planning, geography, civil engineering, spatial science, or mathematics) Demonstrated ability to undertake high-quality research
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of the world to create a Neuro-symbolic AI that can provide assurance guarantees. Required knowledge A successful candidate should have: a degree in Computer Science, Information Technology Engineering, or
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into the learned neural models of the world to create a Neuro-symbolic AI that can provide assurance guarantees. Required knowledge A successful candidate should have: a degree in Computer Science
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and Boulton, 1968; Wallace and Dowe, 1999a; Wallace, 2005) is a Bayesian information-theoretic principle in machine learning, statistics and data science. MML can be thought of in different ways - it
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applicants): one in the Humanities and Social Sciences (HASS) disciplines. one in the Science, Technology, Engineering and Mathematics (STEM) disciplines. Selection criteria Relevance, quality and
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Dowe, 1999a) ensures that - at least in principle, given enough search time - MML can infer any underlying computable model in a data-set. A consequence of this is that we can (e.g.) put latent factor
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that occurs within these biological neural networks, so that these networks can be leveraged for AI applications. In addition, you will develop mathematical and computational neuroscience models
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learning to guide the self-aware learning and network formation. As such this expected to be a purely mathematical and computational project. To do this project you would need to apply for a Monash