39 parallel-computing-numerical-methods positions at Monash University in computer-science in Australia
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Postdoctoral Research Fellow: Computational Methods for Fluid-Granular Flows Job No.: 690226 Location: Clayton campus Employment Type: Full-time Duration: 3-year fixed-term appointment Remuneration
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Research Program Manager, Psychology Job No.: 697561 Location: Clayton campus Employment Type: Full-time Duration: 3 year fixed-term appointment Remuneration: $124,343 - $137,251 pa Hew 8 plus 17
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Program Director, Cyber and Infrastructure Strategic Program Delivery Job No.: 697309 Location: Clayton campus Employment Type: Full-time Duration: 3 year fixed-term appointment Remuneration
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This research aims to design a sustainable framework for optimizing distributed computing systems to enhance performance while minimizing energy consumption. Existing scheduling algorithms often
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, scientific machine learning and AI for science, numerical mathematics, inverse problems and scientific computing. The successful candidate will demonstrate expertise in the design, analysis, and/or application
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Indigenous Graduate Program 2027 - Yurranga Barring Duliin Job No.: 695685 Location: Multiple locations - Clayton, Caulfield, Peninsula and Parkville campuses Employment Type: Full-time Duration: 12
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Program Manager, Entrepreneurship and Innovation Job No.: 696213 Location: Clayton campus Employment Type: Full-time Duration: Continuing appointment Remuneration: $124,343 - $137,251 pa HEW Level
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limited to optimisation, scientific machine learning and AI for science, numerical mathematics, inverse problems and scientific computing. The successful candidate will demonstrate expertise in the design
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Position Title: Research Fellow - HTX Computational Scientist Job No.: 695431 Location: Parkville campus Employment Type: Full-time Duration: 2-year fixed-term appointment Remuneration: $123,138
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We are seeking a motivated PhD candidate to work on unsupervised music emotion tagging within the broader field of affective computing. The project aims to develop reproducible machine learning