415 computational-model-"INSAIT---The-Institute-for-Computer-Science" positions at Monash University
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people who discover them The Opportunity The Research Fellow will be a motivated and talented computational scientist and a key member of Monash University ice sheet modelling research team and SAEF’s
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Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling
Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling Job No.: 683222 Location: Clayton campus Employment Type: Full-time Duration: 3.5 to 4-year fixed
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Indigenous Graduate Program 2026 Job No.: 679754 Location: Multiple locations - Clayton, Caulfield, Peninsula and Parkville campuses Employment Type: Full-time Duration: 12-month fixed-term
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and 45% worldwide. Predictive models based on Artificial Intelligence (AI) techniques will enable risk stratification to implement personalized medicine for prevention and earlier diagnosis of GDM. One
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Associate Professor - Electrical and Computer Systems Engineering Job No.: 678997 Location: Clayton campus Employment Type: Full-time Duration: Continuing appointment Remuneration: $175,487
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Indigenous Initiatives Program Coordinator Job No.: 679410 Location: Clayton campus Employment Type: Full-time Duration: 18 month/year fixed-term appointment Remuneration: $106,789 - $117,128 pa HEW
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Group Manager - Server, Cloud & Compute Platforms Job No.: 680203 Location: Clayton campus Employment Type: Full-time Duration: 3 year fixed-term appointment Remuneration: $184,668 pa HEW level 10
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Recent advancements in foundation models have significantly improved AI systems' capabilities in autonomous tool usage and complex reasoning. However, their potential for location-based and map
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healthcare, finance, environmental monitoring, and beyond. While recent advancements in foundation models have shown tremendous success in NLP and computer vision, the unique characteristics of time series
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computational methods for modelling social dilemmas that can account for real-world complexity in agents’ behaviour. We will build on novel computational techniques to produce realistic enough models that can be