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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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applicants): one in the Humanities and Social Sciences (HASS) disciplines. one in the Science, Technology, Engineering and Mathematics (STEM) disciplines. Selection criteria Monash scholarships are highly
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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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. one in the Science, Technology, Engineering and Mathematics (STEM) disciplines. Selection criteria Relevance, quality and achievability of projects to the Monash-Museums Victoria collaborative research
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, the candidate should ideally possess the following skillset: A strong security background (in cryptography, mathematics, information security, or a related field) Previous industrial experience is not required
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addition, the candidate should ideally possess the following skillset: A strong security background (in cryptography, mathematics, information security, or a related field) Previous industrial experience is
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Number: 04EX783), pp439-444. Molloy, S., D.W. Albrecht, D. L. Dowe and K.M. Ting (2006). Model-Based Clustering of Sequential Data, Proc. 5th Annual Hawaii Intl. Conf. on Statistics, Mathematics and
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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
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into AI systems or mathematical and computational models of brain function. This project would be for someone who wishes to pursue a deeper understanding of humans and machines and the meaning this has
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question and answer component to an existing MAPF visualiser as part of creating XMAPF. Required knowledge - Comfortable with discrete mathematics and proofs - Basic knowledge of AI (e.g., FIT3080