176 evolution-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"IBE-CNR" positions at Monash University in Australia
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I work on the study of massive and supermassive stars (10-100,000 solar masses); the first generations of stars in the universe (Pop III stars); evolution of rotating massive stars and the spin
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the development of Explainable AI Systems that can provide explanations of AI agent decisions to human users. Past work on plan explanations primarily focused on explaining the correctness and validity of plans. In
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of the development of connections in these biological neural networks and their spontaneous and stimulation-driven formation. These models will provide a framework for simulating and testing biological neural networks
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-time Duration: 4-year fixed-term appointment Remuneration: 4-year scholarship package totalling approximately $47,000 per annum tax exempt (2025 rate) A four year Project Expense and Development package
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classification, web scraping, environmental DNA, satellite imagery) and agent-based models and ecological simulations. Our recent work, funded by the Monash Data Futures Institute, has piloted the development
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to concentrate on my academics and professional development. My professional path has been greatly influenced by the opportunities it has created for skill development, including networking events and involvement
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to access leadership and community engagement opportunities and to support the development of emerging engineering leaders to encourage these talented individuals to enter the mining industry upon completion
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applications for monitoring individuals' health, activities and social behaviour. Yet, there is a lack of common and standard guidelines in developing mobile sensing apps (from both software development and UI
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the evolution of massive binary stars into compact binaries as sources of gravitational-waves and astrophysical inference on gravitational-wave observations. My research group on massive binary evolution -- also
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accessibility. In parallel, the role will involve development of a generative AI model capable of predicting reproductive and fertility stages based on biochemical data derived from blood and other biological