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Field
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is a vibrant and progressive place to undertake research. Candidate Requirements The candidate should ideally have a good first degree or a master degree in Computer Science, or a relevant subject
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should hold a very good degree (preferably a masters) in a relevant subject (i.e., Logic, Computer Science, or Mathematics). Candidates with exceptional undergraduate Honours degrees are considered
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of this research will contribute to the growing field of causal reasoning/inference in computer vision. The primary objective of this research is to develop novel techniques for learning causal representations from
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are encouraged. To be considered, please provide the following documents before 30/May/2024: A current curriculum vitae (cv; resume). A full academic transcript (undergraduate, Honours and/or masters degree). A
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theory, the primary objective is to analyse the multifaceted interactions among direct/indirect stakeholders, seeking to uncover how they collaborate and interact to create and foster transformative value
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wellbeing, and an aptitude for mastering advanced quantitative methodologies. The project offers an opportunity to collaborate with researchers at the University of Canterbury, New Zealand, including
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person within a week of shortlisting. Students may consider discussing their internship plans with their Principal Supervisor in anticipation of that.
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the project: Turbulence is the primary cause of frictional losses in transport processes (vehicular transport, distribution pipelines) and the main contributor to drag in wall-bounded flows. Estimates
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can meet the requirements for PhD admission at the University of Adelaide, and who can demonstrate suitable experience in a related field (through a high quality Honours or Masters degree
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of Electrical and Mechanical Engineering, University of Adelaide Associate Professor David Harvey, School of Electrical and Mechanical Engineering, University of Adelaide Dr Phil Wahrlich, Chief Scientist