346 professor-computer "https:" "https:" "https:" "https:" "https:" "Dr" uni jobs at Monash University in Australia
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Monash Mentor Scholarships The Monash Mentor Scholarship is designed to recognise excellence of students participating in the Access Monash Mentoring program. Mentors have the opportunity to develop
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Minds , a leadership program for first year students. Number offered One scholarship available per year Selection criteria Based on academic achievement and need. Preference will be give for a student
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Empirical studies in Information Visualisation research have become more commonplace in the past two to three decades. While formerly the research focus was primarily on utilising the power of novel technologies for presenting data and information in innovative ways, perspectives have changed...
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Planning Systems and Analytics Lead Job No.: 688228 Location: Clayton campus Employment Type: Full-time Duration: Continuing appointment Remuneration: $140,157 - $148,769 pa HEW Level 09 (plus 17% employer superannuation) Amplify your impact at a world top 50 University Join our inclusive,...
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to deliver high-quality technical services in support of our Geomechanics program. In this role, you will: Oversee and conduct a range of standard and specialist geotechnical tests. Train and supervise
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Kenneth Hunt Memorial Scholarship Achieving Potential Scholarship for Distinction The Kenneth Hunt Memorial Scholarship is funded by donors to the University in memory of Professor Kenneth Hunt
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at the University will require the incumbent to hold a valid Working with Children Check. For instructions on how to apply, please refer to “How to apply for Monash Jobs ”. Enquiries: Professor Karen Adams, Director
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Artificial Intelligence (AI) is revolutionizing the field of Magnetic Resonance Imaging (MRI) by enabling faster, more accurate, and cost-effective image reconstruction. This project explores cutting-edge AI methodologies, focusing on combining data-driven approaches with physics-informed models...
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University of Warwick (UK), explores the design and development of computational Decision Support tools to help us better manage the interactions between beneficial insects, such as bees, and the flowering
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. Leveraging techniques such as federated learning, differential privacy, and secure multiparty computation, the goal is to enable collaborative ML tasks without compromising the privacy of individual data